ACS Synthetic Biology最新文献

Characterization of cell-free expression (CFE) systems must expand beyond single spectrophotometric measurements of a green fluorescent protein to provide meaningful metrics of system performance during a CFE reaction and enable the development of predictable and reproducible CFE technologies. To date, comprehensive characterization of these systems has posed a formidable measurement challenge, as it requires time-course measurements of reactions involving endogenous components in addition to transcription and translation of a target genetic circuit added exogenously to the CFE reaction. To provide more informative characterization that is still easy to conduct and complements current practices, we demonstrate a measurement framework for transcription and translation dynamics. We use different nucleic acid templates to characterize a suite of Escherichia coli extracts prepared in-house, as well as extracts and reconstituted systems available commercially. Notably, we include measurements of low-performing systems to assess the sensitivity of our measurement framework and elucidate metrics indicative of system performance. For all these CFE systems, we compute reaction metrics to enable quantitative comparison. We believe this is an accessible measurement framework that can complement existing characterization, provide informative data for developing CFE technologies, and be adopted for routine characterization.

The development of robust, food-grade microbial chassis with tailored metabolic functions is critical for advancing synthetic biology applications in health and nutrition. Here, we report a dual genome engineering strategy that integrates CRISPR-Cas9-mediated knock-in with Cre/loxP-driven genome reduction to streamline the genome of Lactococcus lactis NZ9000 and enable stable expression of a high-activity uricase variant. The resulting strain, NZ9000::UA^T-ΔD6, demonstrated enhanced enzymatic performance in vitro, achieving 2.34 U/mL activity and complete degradation of ∼500 μM urate within 20 h. Beyond improved catalytic output, this dual-system approach established a genetically stable and biosafe probiotic chassis with moderate colonization capacity in the murine gut. The integration of CRISPR-Cas and Cre/loxP techniques in this work is intended to enhance the expression of heterologous genes in the chassis strain, while providing a versatile platform for the rational design of food-grade probiotics and offering a general strategy for constructing living biotherapeutic agents with targeted metabolic activities.

Plasmids are essential tools in molecular biology and biotechnology. In research laboratories, it is common to use antibiotic selection markers to ensure that plasmids are stably maintained in a cellular population. However, the use of antibiotics poses a significant challenge in the industrial scale-up process due to the high cost and the risk of spreading resistance. Therefore, methods for antibiotic-free plasmid maintenance are in high demand. Here, we present an essential gene-based plasmid selection strategy utilizing the Escherichia coli tryptophan tRNA (trpT) gene. We developed a workflow using a base strain with a trpT deletion and a temperature-sensitive trpT-expressing plasmid to circumvent the need for remaking chromosomal trpT deletions for every transformation. We evaluated the stability of a range of antibiotic gene-free trpT plasmids with different copy numbers and determined that the system is as efficient as, or better than, systems using antibiotics. Furthermore, the system is stable when producing a biochemical at industrially relevant fermentation conditions, and due to the small size of trpT, it allows for plasmid minimization. The approach constitutes a significant contribution toward developing simpler and more effective antibiotic-free bioprocesses and combating the spread of multiresistant infections.

The widespread use of polypharmacy has significantly increased the risk of drug–drug interactions (DDIs), underscoring the critical need for developing accurate drug–drug interaction events (DDIEs) prediction methods. However, current DDI studies inadequately account for the intrinsic relationships between atoms and bonds in drug molecules, while also overlooking the three-dimensional conformational information on these molecules. To address these limitations, we propose ABT-DDI, an innovative DDI prediction model based on a graph transformer architecture, capable of extracting multimodal information from drug molecules to predict DDI risk levels. ABT-DDI introduces the pioneering systematic modeling of spatial relationships including atom–atom, atom–bond, and bond–bond interactions through a multiscale attention mechanism, which effectively captures atomic and bonding interaction patterns to enhance substructure perception. Furthermore, we introduce two dedicated virtual nodes representing global atom and bond embeddings, which systematically aggregate and propagate overall structural information to refine high-level feature learning. Additionally, the model integrates molecular fingerprint features with 3D spatial distance descriptors to establish a comprehensive molecular representation system. Experimental results demonstrate that our model significantly outperforms existing state-of-the-art methods across multiple metrics on two benchmark data sets, showing important application value in drug development and polypharmacy risk warning systems.

Modern genome editing methods permit the flexible modification of organisms at the genome level. However, bacteriophages, despite their small genomes, pose unique challenges due to the need to edit during their infection cycle, then select/screen for the modified genomes against a background of the wild type phage. Direct genome synthesis enabled by High-Complexity Golden Gate Assembly (HC-GGA) offers an alternative approach that permits rapid, accurate, and flexible genome modification. Here, we demonstrate HC-GGA’s bacteriophage engineering potential, particularly in addressing the public health challenge of detecting hazardous pathogens and nonpathogenic bacteria as indicators of fecal contamination (indicator organisms) in water supplies. A bacteriophage-based biosensor was developed by recoding the genome to enable in vivo incorporation of the alkyne-modified noncanonical amino acid L-homopropargylglycine into the capsid. The modification enabled a bio-orthogonal cycloaddition reaction with azide-conjugated magnetic nanoparticles resulting in magnetized phages which were able to bind, capture, and concentrate their host E. coli. In parallel, the engineered phage expressed luciferase during infection, allowing detection of E. coli at concentrations below 10 CFU per 100 mL in drinking water samples. The approach significantly reduces assay time and cost associated with such assays, particularly in field-based applications, thereby illustrating the practical benefits of synthetic biology in environmental monitoring and public health initiatives.

A hybrid gene cluster, mfq, predicted to govern the biosynthesis of both meroterpenoids and phenaziterpenes, was cloned from the genome of Streptomyces sp. S4.7 and introduced into the heterologous host Streptomyces coelicolor M1154. The biosynthesis of the meroterpenoids marfuraquinocins C and D, previously isolated from Streptomyces niveus SCSIO 3406, as well as a new congener, marfuraquinocin E, which exhibited antibacterial activity, was activated upon overexpression of the regulatory protein MfqF. However, production of neither phenaziterpenes nor phenazines was detected. The structure of marfuraquinocin E was elucidated, revealing the attachment of a terpene moiety at C-2, in contrast to C-6 as seen in the known congeners A–D. Using the CRISPR/Cas9 system, several genes in the mfq cluster were inactivated, confirming the role of MfqW as a prenyltransferase specific to the meroterpenoid pathway. Both gene overexpression and further knockouts provided the first insights into the complex regulation of this hybrid gene cluster. To restore the presumably deficient phenazine biosynthetic pathway, a gene encoding a PhzF homologue from another gene cluster in S4.7 was heterologously expressed alongside the mfq cluster, leading to the production of 1,6-phenazine dicarboxylic acid upon MfqF overexpression. This work lays the foundation for elucidating the complete biosynthetic pathway of marfuraquinocins and its potential coregulation with that of phenazines.

Microbiomes, complex communities of microorganisms and their genetic material, hold immense potential for addressing global challenges in diverse sectors, including healthcare, agriculture, and bioproduction. Engineering these intricate ecosystems, however, necessitates a comprehensive understanding of the complex web of microbial interactions. The emergence of machine learning (ML) has revolutionized microbiome research, offering powerful tools to analyze massive data sets, uncover hidden patterns, and predict microbial behavior. ML algorithms have demonstrated remarkable success in identifying and characterizing microbial communities, predicting interactions between organisms and optimizing the design of microbial communities for specific functions. This Perspective examines the transformative applications of ML in the context of microbiome engineering, encompassing both microbiome data analysis and the targeted manipulation of microbial communities. These techniques employ a variety of strategies, including the manipulation of quorum sensing molecules, antimicrobial peptides, growth conditions, the introduction of probiotics, and the utilization of bacteriophages. By integrating ML with experimental approaches, researchers are pushing the boundaries of microbiome engineering, paving the way for novel applications in diverse fields. However, it is important to acknowledge the challenges that ML algorithms face, such as the limited availability of high-quality, large-scale data sets, the inherent complexity of biological systems, and the need for improved integration of experimental and computational methods. This perspective further discusses the future perspectives of the field, highlighting expected developments in data generation, algorithm development, and interdisciplinary collaboration. These advancements hold the key to unlocking the full potential of microbial communities for addressing pressing global challenges.

Rubisco catalyzes the CO₂ fixation step in the dark reactions of photosynthesis. Transgenic expression of better-performing Rubisco orthologs in plants or discovery of improved mutants of Rubisco via protein engineering could theoretically accelerate plant growth and improve crop yields. However, efforts to heterologously express or engineer Rubisco are frequently stymied by the chaperone-dependent folding and assembly of the Rubisco holoenzyme, a process that can be disrupted by changes to Rubisco’s sequence. Elucidation of the effects that alterations to Rubisco’s sequence impose upon its biogenesis is hampered by reliance upon low-throughput methods for verification of Rubisco assembly. Here, we report the engineering of a genetically encoded biosensor to sense the assembly of Form I Rubiscos in E. coli. We show that the biosensor can detect the RbcS-dependent assembly of cyanobacterial Rubisco orthologs, the formation of chaperone-stabilized RbcL oligomeric assembly intermediates, and differences in assembly caused by mutations to the RbcL sequence. Additionally, we perform a large-scale examination of the relative assembly levels of a ∼7500-member Halothiobacillus neapolitanus RbcL mutant library by adapting the biosensor for use with phage-assisted noncontinuous selection. Our experiment predicts that the majority (>90%) of examined RbcL mutations exert a negative effect on assembly, lending support to the hypothesis that Rubisco biogenesis constrains both its natural evolution and improvement by protein engineering.

Recombinant adeno-associated viral (rAAV) vectors are a leading platform for in vivo gene therapy, valued for their excellent safety, broad serotype diversity, and scalable production. Targeted delivery through capsid display of ligands holds great promise, yet current retargeting strategies often rely on extensive capsid re-engineering and restrict the use of ligands incompatible with intracellular expression systems. Here, we present a modular AAV retargeting platform that, for the first time, employs the SpyTag/SpyCatcher system via genetic integration into the AAV2 capsid. SpyTag is a small peptide that forms a covalent, irreversible bond with its protein partner, SpyCatcher, allowing site-specific ligand coupling under physiological conditions. Inserting SpyTag into surface-exposed capsid sites enabled postassembly functionalization of AAVs with SpyCatcher-fused targeting proteins. As proof of concept, we used SpyCatcher fusions with designed ankyrin repeat proteins (DARPins) specific for EGFR, EpCAM, and HER2. This conferred highly specific transduction of corresponding cancer cell lines with minimal off-target activity. Therapeutic potential was demonstrated by delivering a suicide gene, inducing selective cancer cell killing upon prodrug administration. This “one-fits-all” platform allows rapid and flexible retargeting without significantly altering the underlying vectors genome or production process. It supports the incorporation of large or complex ligands not amenable to genetic fusion and facilitates high-throughput preclinical evaluation strategies. By uniting capsid engineering with modular ligand display, our approach provides a scalable and versatile framework for precision gene delivery, broadening the applicability of rAAV in both therapeutic and discovery settings.

Enzymes are biological catalysts that speed up chemical reactions in an eco-friendly way. Precise enzyme design is hindered by vast sequence space and intricate sequence–structure–function interdependencies. To address these challenges, we developed EvoZymePro-Cat (EZPro-Cat), a deep learning platform for enzyme mutant screening. Conventional methods for predicting absolute mutant activities suffer from systematic errors and limited generalizability. Our pairwise comparison framework directly models relative activity superiority between variants, eliminating dependence on absolute value predictions. The framework integrates full sequence and local structure semantics of protein and ligand information using bilinear attention mechanisms. Protein sequences are encoded using the ESM1b transformer model. Ligands are represented through MolT5 embeddings and MACCS molecular fingerprints. The adaptability of protein residues to their microenvironments is captured by integrating structural features and site-specific evolutionary characteristics. Bilinear attention mechanisms capture long-range intermolecular interactions during catalysis by bidirectional projection and weighted fusion of protein–ligand features. Compared to existing methods, our model exhibits superior performance in identifying improved enzyme mutants through comparative prediction of mutation effects on activity, such as K_m and k_cat. For deep mutation scanning data sets, a few-shot learning strategy combined with the EZPro-Cat framework boosts prediction precision (AUC 0.908). By using integrated multimodal representations, EZPro-Cat offers a mechanistic and practical solution for functional profiling of intraprotein variants, driving paradigm shifts in highly efficient enzyme discovery and directed evolution.