ACS Synthetic Biology最新文献

Efficient transcription factor (TF) screening is essential for the rational optimization of industrial microbial strains. Here, we present an absorbance-activated laccase-based biosensor (AALB) platform designed for rapid functional screening of TFs in Trichoderma reesei. By using laccase as a reporter, the AALB platform offers a robust and cost-effective screening system for TF functional evaluation. The secreted heterologous laccase also complements T. reesei’s native cellulolytic machinery, expanding its enzymatic repertoire for lignocellulosic biomass deconstruction. The core of the AALB platform is a Pcbh1-regulated laccase expression cassette, enabling sensitive and efficient selection of TF variants based on absorbance readouts. To further improve sensitivity, we introduced a quadruple repeat of the cis-element-dense segment in the cbh1 promoter and integrated a high-activity heterologous laccase at the SLP1 site of the T. reesei genome. Following a miniaturized 2 day pre-culture in 24-well plates, the AALB platform enables TF variant ranking within 30 min of absorbance readout in 200 μL 96-well reactions, reducing the need for extended shake-flask fermentation and separate downstream enzymatic or molecular assays during the initial screen. Using AALB, we successfully identified TF variants with robust transcriptional regulation. The screening results were validated through transcript quantification of laccase and XYR1 and extracellular laccase activity assays. Notably, the XYR1 mutation XYR1_V821F, selected via AALB, significantly increased laccase transcript levels compared to PoLAC-SLP1 chassis control, with laccase activity reaching 17.74 ± 0.76 IU/mLa 2.7-fold improvement. Further characterization of the AALB-selected XYR1_V821F strain showed increased extracellular laccase activity together with increased xylanase and filter paper activities, with extracellular xylanase and filter paper activities increased by 2.7- and 1.9-fold, respectively, and enhanced rice straw hydrolysis under the tested fermentation and hydrolysis conditions. The AALB platform provides valuable insights into the synthetic biology and metabolic engineering of T. reesei, facilitating the development of industrial strains for lignocellulosic biomass processing and enzyme production.

Outer membrane vesicles (OMVs), as promising delivery vectors, have been extensively explored for the delivery of nucleic acid-based drugs, particularly the RNA therapy. However, RNA-loaded OMVs delivery systems derived from in vivo engineered bacteria lack the controllable way for regulating target RNA synthesis. Constitutive RNA synthesis may not only exert excessive metabolic burden on engineered bacteria but also lead to unexpected RNA package and delivery, markedly compromising in vivo biomedical applications. Herein, we developed an optogenetically regulated small interfering RNA (siRNA) synthesis and delivery system based on engineered bacterial OMVs. A proof of concept was demonstrated for combining this optogenetic siRNA delivery system with photothermal therapy (PTT) to evaluate tumor thermal resistance modulation by using dual functional engineered strains. The first strain (opto-siTRPV1@OMVs BL-ves) was designed to secrete siRNA targeting transient receptor potential vanilloid subtype 1 (TRPV1) in response to green light stimulation, while a second melanin-producing strain (BL21-TYR) was established for efficient photothermal ablation under 808 nm near-infrared (NIR) laser irradiation. Experimental results showed that the combined therapeutic regimen demonstrated a preliminary tumor growth inhibition rate of approximately 63.06% in a murine model. Furthermore, this regimen remodeled the immunosuppressive tumor microenvironment (TME), activated antigen-presenting cells, promoted the infiltration of effector T cells, and thereby triggered robust anti-tumor immune responses. This study presents a novel strategy for the precise and regulated delivery of siRNA, and demonstrates the feasibility of optogenetically controlled siRNA production and OMV-mediated delivery in a laboratory setting.

Benzylisoquinoline alkaloids (BIAs) are pharmacologically valuable natural products, yet their microbial production in Escherichia coli is hindered by two major bottlenecks: (1) imbalanced metabolic flux between the two upstream precursors, 3,4-dihydroxyphenylacetaldehyde (3,4-DHPAA) and dopamine, and (2) poor soluble expression of plant-derived enzymes, particularly norcoclaurine synthase (NCS) and the methyltransferase Ps4′OMT2. To address these challenges, we systematically engineered E. coli for efficient synthesis of (S)-reticuline, the universal precursor to diverse BIAs. First, to resolve the flux imbalance, we introduced a heterologous ω-transaminase (ω-TA) that dynamically interconverts 3,4-DHPAA and dopamine, thereby coordinating precursor supply without tedious tuning of branch pathways. Second, to overcome enzyme insolubility, we combined solubility-enhancing strategiesincluding SUMO fusion, ProteinMPNN-guided design, and N-terminal truncationwhich markedly improved the soluble expression of CjNCS and Ps4′OMT2. Furthermore, strengthening the S-adenosylmethionine (SAM) regeneration cycle through integration of luxS, mtn, and metF genes enhanced the efficiency of the three sequential methylation reactions. The final engineered strain achieved a (S)-reticuline titer of 347 mg/L in shake-flask fermentation. Overall, this work establishes an efficient E. coli chassis for sustainable BIAs production by systematically tackling flux imbalance and enzyme solubility.

Bacteriophages represent a largely untapped reservoir of regulatory elements that can expand the genetic toolbox for engineering non-model hosts. Using ONT-cappable-sequencing, we mapped full-length primary transcripts across five Pseudomonas phages and identified 232 transcription start sites (TSSs) and 176 transcription termination sites (TTSs), including 38 intrinsic factor-independent terminators. Motif discovery revealed 59 σ70-like promoters in KIL5, 37 in KIL3b, and unique phage-encoded RNA polymerase (RNAP) promoter motifs for KNP, ϕ2, and pphageB21, enabling discrimination between host- and phage-dependent transcriptional control. To validate these identifications, a subset of ten promoters and five terminators was quantitatively characterized in vivo in Pseudomonas fluorescens strain GL-S-306, showing promoter activities spanning a large dynamic range and terminator efficiencies allowing complete transcriptional termination. These data establish the first experimentally validated, phage-derived library of promoters and terminators for P. fluorescens and Pseudomonas syringae, providing chassis-specific regulatory parts that enable more finetuned gene-expression than current cross-species SynBio tools. Our results position phage transcriptomics as a scalable strategy for mining regulatory elements tailored to non-model bacterial hosts, while also supporting phage design and engineering efforts and accelerating the development of new synthetic biology platforms.

The integration of artificial intelligence with protein engineering has created unprecedented opportunities for enzyme discovery and the enhancement of overall catalytic performance. In this study, a novel nitrilase, BhNIT, was identified from Bradyrhizobiumhuanghuaihaiense through a computationally assisted screening strategy. Using BhNIT as the target protein for engineering, we established DeepPCD as an integrated computationally guided workflow that organizes sequence-design, structure-filtering, stability-assessment, evolutionary-compatibility, and molecular dynamics analyses to prioritize experimentally testable mutations. Compared with approaches that require extensive experimental screening of large mutant libraries, the DeepPCD workflow facilitates efficient enzyme engineering modifications. By modifying only seven candidate residues of BhNIT, we obtained a mutant exhibiting a 1.3 × 10⁵-fold improvement in hydrolytic efficiency. In addition, this mutant showed a 20.4°C increase in melting temperature and a broader operational pH range extending toward more alkaline conditions, substantially enhancing its potential for industrial applications. Molecular dynamics simulations and structural analysis revealed that the enhanced performance stems from effective reshaping of the substrate-binding pocket and access channel, along with a strengthened interaction network. Furthermore, preliminary computational application to additional nitrilases suggested the potential transferability of this workflow within the nitrilase family. Overall, this work provides an experimentally validated case study for computationally guided nitrilase engineering and offers a practical workflow for prioritizing mutations that may improve activity and stability, although broader generalization will require further validation across more diverse enzyme systems.

The rapid growth of genomic and metagenomic data has created a large gap between protein sequence discovery and enzyme functional characterization. Deep learning has become an important tool for enzyme-related prediction tasks, including EC and GO annotation, substrate specificity, catalytic-residue identification, kinetic-parameter estimation, thermostability, pH optima, and candidate discovery. This review summarizes recent progress with an emphasis on benchmark design rather than reported scores alone. We compare representative methods by input modality, training data, split strategy, leakage control, evaluation metric, and validation evidence. Across tasks, apparent performance gains can arise from dataset scale, annotation quality, homolog overlap, negative sampling, or endpoint definition rather than architecture alone. Current models are valuable for annotation and candidate prioritization, but robust discovery of remote homologs, rare functions, or new catalytic activities still requires low-identity or out-of-distribution evaluation and experimental validation.

Escherichia coli is a well-established model organism in molecular biology and biotechnology. Despite its long history as a laboratory workhorse, the efficient single-step chromosomal integration of large DNA fragments remains a challenge. Currently known methods are either simple but have limitations on insert size, or flexible but laborious requiring plasmid construction or multi-step procedures. Here, we present PhAGE (Phage-Assisted Genome Engineering), which enables the integration of ∼20 kb DNA fragments into the E. coli genome within a single day. PhAGE method uses in vitro packaging of recombinant DNA into bacteriophage capsids, followed by general transduction to introduce pre-assembled DNA with flanking homology arms into recipient cells. This approach allows efficient and landing pad-free integration of large constructs into the target loci. We demonstrate its usefulness through rapid integration of multi-gene operons. PhAGE resolves the long-standing trade-off between simplicity and insert size in E. coli genome engineering, providing a powerful assembly-to-integration toolkit that accelerates strain construction across a wide range of applications, from biosynthetic pathway engineering to genome-scale design.

The fifth annual virtual minimal cell workshop, hosted by the J. Craig Venter Institute (JCVI), brought together an international community of researchers from academic, industrial, and government laboratories. The 2025 meeting featured 30 talks focused on using minimal bacterial cells to probe the fundamental principles of life, develop new construction strategies, and advance predictive frameworks for cellular behavior. Most of the presentations centered on the JCVI minimal bacterial cells JCVI-syn3.0, JCVI-syn3A, and JCVI-syn3B. Multiple talks addressed the persistent set of essential genes with unknown or partially defined functions, leveraging advanced imaging, computational modeling, and experimental evolution to uncover their cellular roles. Studies of protein moonlighting, membrane biophysics, cell division, metabolism, and aging demonstrated how minimal cells expose fundamental biological constraints often obscured in more complex organisms. Whole-cell computational models increasingly reproduce growth, division, metabolism, gene regulation, and macromolecular assembly. Presentations also highlighted new approaches to whole-genome transplantation, development of alternative minimal genome chassis, synthetic cell assembly from non-living components, chromosome-scale minimization in eukaryotes, and conceptual agricultural and prebiotic systems. Collectively, these efforts position minimal cells as powerful platforms for understanding, building, and predicting living systems.

Characterizing CRISPR interference (CRISPRi) phenotypes presents a fundamental temporal challenge: pre-existing overabundance of target proteins can mask early silencing, requiring extended growth for dilution, yet prolonged repression rapidly selects for escaper mutants. To resolve this, we integrated a tightly regulated CRISPRi system in Pseudomonas putida with an automated mini bioreactor platform operating in turbidostat mode. By maintaining continuous exponential growth, we mapped the exact temporal dynamics of essential gene silencing. We identified a critical observation window between 7.8 and 26.7 h (corresponding to 6.4−23.5 cumulative cell doublings of the uninduced condition) where repression exerts its maximum physiological impact, directly preceding population takeover by target-site mutated escapers. Applying this workflow to the arginine biosynthesis pathway, multi-omics profiling disentangled transient physiological buffering from long-term mutational events, revealing that argH and argG knockdowns trigger highly diverse metabolomic perturbations. This scalable framework overcomes batch culture limitations for CRISPRi experiments, ensuring precise temporal control for accurate phenotypic characterization and reliable functional genomics.