ACS Synthetic Biology最新文献

Synthetic biology laboratories generate diverse forms of data and metadata throughout a project’s life cycle, such as sequences, models, protocols, images, and time-series measurements. Unfortunately, these assets are scattered across spreadsheets, proprietary exports, custom scripts, etc. found in varied locations such as shared drives. Inconsistent metadata and data formats hinder provenance, reuse, security, compliance, automation, and scale-up. The central gap is a coherent way to link data, metadata, and code so they remain findable, accessible, interoperable, and reusable (FAIR). This perspective considers current practices through semistructured interviews with synthetic biology researchers in laboratories across the United States, and the findings were used to provide guidance to create a framework for an integrated data management workflow. This framework maps common data types to community standards that allow machine-accessible metadata, version control, and standards-compliant repositories. This perspective also offers a catalog of potential software solutions and stepwise adoption guidelines that turn the proposed framework into a day-to-day practice, democratizing the generation of standardized data. The result is that users gain a template that raises data to FAIR status, strengthens traceability for regulatory or defense contexts, and provides a stronger foundation for training machine learning models.

Dynamic programming algorithms within the NUPACK software suite enable analysis of equilibrium base-pairing properties for complex and test tube ensembles containing arbitrary numbers of interacting nucleic acid strands. Currently, calculations are limited to single-material systems that are either all-RNA or all-DNA. Here, to enable analysis of mixed-material systems that are critical for modern applications in vitro, in situ, and in vivo, we develop physical models and dynamic programming algorithms that allow the material of the system to be specified at nucleotide resolution. Free energy parameter sets are constructed for both RNA/DNA and RNA/2′OMe-RNA mixed-material systems by combining available empirical mixed-material parameters with single-material parameter sets to enable treatment of the full complex and test tube ensembles. New dynamic programming recursions account for the material of each nucleotide throughout the recursive process. For a complex with N nucleotides, the mixed-material dynamic programming algorithms maintain the O(N³) time complexity of the single-material algorithms, enabling efficient calculation of diverse physical quantities over complex and test tube ensembles (e.g., complex partition function, equilibrium complex concentrations, equilibrium base-pairing probabilities, minimum free energy secondary structure(s), and Boltzmann-sampled secondary structures) at a cost increase of roughly 2.0–3.5×. The results of existing single-material algorithms are exactly reproduced when applying the new mixed-material algorithms to single-material systems. Accuracy is significantly enhanced using mixed-material models and algorithms to predict RNA/DNA and RNA/2′OMe-RNA duplex melting temperatures from the experimental literature as well as RNA/DNA melt profiles from new experiments. Mixed-material analyses can be performed online using the NUPACK web app (www.nupack.org) or locally using the NUPACK Python module.

As the most important carbon source for bioproduction, glucose is transported and enters the central carbon metabolic pathway directly in most microorganisms. However, certain bacteria utilize membrane-bound dehydrogenases to oxidize glucose to gluconic acid (GA) in the periplasmic space. GA is subsequently converted either to 5-keto-d-gluconate by PQQ-dependent gluconate dehydrogenase (GADH) or to 2-keto-d-gluconate by FAD-dependent GADH, with the latter further oxidized to 2,5-diketo-d-gluconate. This review systematically examines the composition, distribution, physiological functions, and key enzymes of this oxidative pathway, alongside industrial applications of its metabolic products. Special emphasis is placed on metabolic engineering strategies─including bottleneck elimination, cofactor balancing, and chassis optimization─to overcome inherent regulatory constraints and enhance carbon flux toward target compounds. The potential of this pathway for the sustainable production of tartaric acid, 2,5-furandicarboxylic acid, vitamin C precursors, and phosphorus fertilizers is comprehensively assessed.

Nucleic acid (NA) hybridization reactions are central to molecular biology and NA nanotechnologies, which exploit base-pairing to generate intricate structures and dynamic reaction systems. NA chaperones are molecules (typically proteins) that can catalyze the formation of thermodynamically favorable NA structures through inter- and intramolecular hybridization. This work develops smart NA chaperones (SNACs) which combine the catalytic properties of NA chaperones with the dynamic behavior of intrinsically disordered proteins (IDPs) engineered to undergo programmable, triggered, and reversible liquid–liquid phase separation. We present model SNACs that are fusions of NA chaperones and IDPs, or are IDPs that have been designed to have NA chaperoning function. We demonstrate the ability of SNACs to enhance the kinetics of nucleic acid strand annealing (SA) and toehold-mediated strand displacement (TMSD) reactions. In the case of SA, SNACs, including engineered elastin-like proteins fused with NA-binding domains, overcome kinetic barriers in the hybridization of structured ssDNA oligonucleotides and form protein–DNA coacervates that accelerate hybridization reactions. Similarly, SNACs significantly enhance TMSD kinetics, both in their soluble state and within phase-separated, SNAC coacervates. This work establishes SNACs as tools for controlling NA hybridization, leveraging phase-separation to enhance reaction kinetics, and highlighting their potential as nanoassemblers for complex NA systems.

G-protein-coupled receptors (GPCRs) are high-value therapeutic targets, yet antibody discovery remains limited by difficulties in preparing antigens that preserve native conformations. Here, we engineered a native-like, full-length human LPA2 antigen by combining N-terminal P9* fused with amphipathic poly-γ-glutamate (APG) stabilization, affording an antigen suitable for the selection of antibodies with therapeutic efficacy. By screening a large synthetic human scFv library, we isolated an antagonistic antibody against LPA2 that bound LPA2 selectively over LPA1 (EC₅₀: 4.5 nM for IgG; 30 nM for scFv). The discovered antibody inhibited the growth of SKOV-3, an ovarian cancer cell line, in vitro by reducing the phosphorylation of multiple signaling pathways, including p38, protein kinase B (Akt), and signal transducer and activator of transcription 3 (STAT3), with particularly strong suppression of extracellular signal regulated kinase (ERK) and c-Jun N-terminal kinase (JNK). In vivo, the antibody suppressed tumor growth in SKOV-3 xenografted BALB/c nude mice without affecting body weight. Furthermore, computational docking revealed that the anti-LPA2 antibody blocked LPA-mediated downstream signaling by masking the ligand-binding pocket of LPA2. Collectively, these results validate an engineering-first strategy for generating native-like GPCR antigens that enable the discovery of functional anti-GPCR antibodies with therapeutic efficacy and is readily generalizable to other challenging membrane targets.

p-Coumaric acid is a valuable phytochemical with significant roles in anticancer cell proliferation, antianxiety, and neuroprotection and as a key precursor for various flavonoids. However, the production of p-coumaric acid in microorganisms is often limited by enzyme compatibility and its antimicrobial effects. In this study, a p-coumaric acid producing Escherichia coli strain was constructed. First, the cryptic plasmids pMUT1 and pMUT2 were eliminated from E. coli Nissle 1917 by using the CRISPR/Cas9 method to mitigate their interference with heterologous gene expression, and the resulting strain WEN01 was used to screen for the genes encoding for tyrosine ammonia-lyase with superior host compatibility. Next, the gene tyrR encoding a global regulator was knocked out to alleviate the repression of l-tyrosine production. The key genes pheL and pheA involved in phenylalanine biosynthesis were knocked out to reduce byproduct formation, resulting in the strain WEN06. Finally, the quorum sensing system was used to overexpress the key genes aroG^fbr and tyrA^fbr in the l-tyrosine biosynthetic pathway, and the resulting strain WEN06/pWT101-AT, pWT104F could produce 462.6 mg/L p-coumaric acid in shake flask fermentation. In fed-batch fermentation, the engineered strain WEN06/pWT101-AT, pWT104F could produce 10.3 g/L p-coumaric acid with a glucose conversion yield of 0.13 g/g and a productivity of 0.14 g/L/h. This work provides a novel strategy for the efficient production of p-coumaric acid and lays a foundation for the efficient production of antimicrobial natural products in bacteria.

In synthetic biology, a key goal is to design robust and stable genetic circuits with accurate and predictable behavior. Modularity is a central principle in circuit design, enabling the construction of complex systems from smaller, well-characterized parts. However, resource competition presents a major obstacle to modularity by disrupting gene expression dynamics. Here, we constructed and characterized a library of inhibitory genetic cascades with varied promoter strengths, RBS strengths, and plasmid backbones. We found that increasing the expression of the downstream module could unexpectedly lead to a reduction in the expression of the upstream regulatory module by competing for shared cellular resources. These results indicate that resource limitations can transform a unidirectional inhibitory cascade into an unintended feedback loop. In addition, we found that growth-mediated dilution can reshape gene expression patterns, further influencing circuit dynamics. Together, these findings underscore the critical roles of both resource competition and growth dilution in shaping the behavior of synthetic gene circuits.

Bacillus licheniformis is a spore-forming bacterium with probiotic, environmental, and industrial applications. Many wild strains with diverse functions have been described in recent years. Nevertheless, the lack of efficient and universal genetic manipulation tools hinders the study and engineering of these strains. Here, a versatile and simple genetic manipulation toolkit is established for B. licheniformis. The cornerstone of this toolkit is a conjugative DNA transfer system. This system could effectively transfer temperature-sensitive plasmid pTSMK into all ten tested B. licheniformis strains, with efficiencies ranging from 10^–5 to 10^–3. Based on this DNA transfer system, the tools for maker-free knockout and knock-in, CRISPRi, as well as transposon mutagenesis, were built. A transposition frequency of 7.68 × 10^–3 was observed. The toolkit developed in this study fulfills most tasks in the engineering of this species and will promote the basic and applied research of B. licheniformis.

Cyanobactin biosynthetic pathways are used in synthetic biology approaches to create large, peptide-based chemical libraries with drug-like features such as N–C macrocyclization and prenylation. It remains challenging to express enzymes from multiple RiPP pathways to rationally produce the desired products. Here, we designed a simple yet robust method aimed to produce and assess multiple enzymes, fusing biosynthetic genes together in a well-expressed, soluble construct that enables production of macrocyclic peptides and selectively appends C₅, C₁₀, or C₁₅ isoprenoids to tyrosine side chains. A library was developed and assayed, defining the sequence features necessary for prenylation and providing an overall >40% success rate of using a library with an estimated maximum size of 2.6 million peptide derivatives. This flexible and robust system enables the generation of novel compounds and libraries of such compounds with minimal side products in living organisms.

Monoclonal antibodies (mAbs) constitute a leading class of biotherapeutics, and meeting global demand requires manufacturing platforms that deliver rapid and reliable expression. Nevertheless, efficient and predictable production remains challenging because multiple vector elements can substantially influence productivity and product quality. In this study, we examined how sequence design features influence expression in a targeted-integration CHO system using six antibodies (A–F). We evaluated codon usage in the constant light (CL) and constant heavy (CH) regions with and without LALA/YTE Fc mutations, the type of signal peptide, codon optimization of the variable regions, and substitution of the kappa light chain with lambda. We found that changing codons of CL and CH to suboptimal codons reduced expression for all six antibodies, independent of LALA/YTE status. Replacing chain-specific signal peptides with a single identical peptide produced antibody-specific outcomes: productivity decreased for four antibodies (A, C, D, and E), increased for antibody B by ∼1.5-fold, and remained unchanged for antibody F. In addition, codon optimization of the variable regions generally enhanced productivity in an antibody-dependent manner, with improvements ranging from ∼2-fold to ∼18-fold. Finally, substituting kappa with lambda decreased productivity for three antibodies, suggesting a CHO cell preference for the kappa isotype. Collectively, these findings delineate practical sequence-engineering principles for CHO expression, prioritize codon usage in constant and variable domains, maintain chain-appropriate signal peptides, and account for kappa/lambda dependencies, thereby improving construct selection and accelerating development of high-yielding mAb producers.