ACS Synthetic Biology最新文献

Herein, we report macroscopic materials formed by the assembly of engineered bacterial spores. Spores were engineered by using a T7-driven expression system to display a high density of pH-responsive self-associating proteins on their surface. The engineered surface protein on the spore surface enabled pH-dependent binding at the protein level and enabled the assembly of granular materials. Mechanical properties remained largely constant with changing pH, but erosion stability was pH-dependent in a manner consistent with the pH-dependent interaction between the engineered surface proteins. Our finding utilizes synthetic biology for the design of macroscopic materials and illuminates the impact of coiled-coil interaction across various length scales.

l-Threonine, an essential amino acid, is widely used in various industries, with an annually growing demand. However, the present Corynebacterium glutamicum strains are difficult to achieve industrialization of l-threonine due to low yield and purity. In this study, we engineered an l-isoleucine-producing C. glutamicum WM001 to efficiently produce l-threonine by finely regulating the carbon flux. First, the threonine dehydratase in WM001 was mutated to lower the level of l-isoleucine production, then the homoserine dehydrogenase and aspartate kinase were mutated to release the feedback inhibition of l-threonine, and the resulting strain TWZ006 produced 14.2 g/L l-threonine. Subsequently, aspartate ammonia-lyase and aspartate transaminase were overexpressed to accumulate the precursor l-aspartate. Next, phosphoenolpyruvate carboxylase, pyruvate carboxylase and pyruvate kinase were overexpressed, and phosphoenolpyruvate carboxykinase, oxaloacetate decarboxylase were inactivated to fine-regulate the carbon flux among oxaloacetate, pyruvate and phosphoenolpyruvate. The resulting strain TWZ017 produced 21.5 g/L l-threonine. Finally, dihydrodipicolinate synthase was mutated with strong allosteric inhibition from l-lysine to significantly decrease byproducts accumulation, l-threonine export was optimized, and the final engineered strain TWZ024/pXTuf-thrE produced 78.3 g/L of l-threonine with the yield of 0.33 g/g glucose and the productivity of 0.82 g/L/h in a 7 L bioreactor. To the best of our knowledge, this represents the highest l-threonine production in C. glutamicum, providing possibilities for industrial-scale production.

The invention of RNA-guided DNA cutting systems has revolutionized biotechnology. More recently, RNA-guided RNA cutting by Cas13d entered the scene as a highly promising alternative to RNA interference to engineer cellular transcriptomes for biotechnological and therapeutic purposes. Unfortunately, "collateral damage" by indiscriminate off-target cutting tampered enthusiasm for these systems. Yet, how collateral activity, or even RNA target reduction depends on Cas13d and guide RNA abundance has remained unclear due to the lack of expression-tuning studies to address this question. Here we use precise expression-tuning gene circuits to show that both nonspecific and specific, on-target RNA reduction depend on Cas13d and guide RNA levels, and that nonspecific RNA cutting from trans cleavage might contribute to on-target RNA reduction. Using RNA-level control techniques, we develop new Multi-Level Optimized Negative-Autoregulated Cas13d and crRNA Hybrid (MONARCH) gene circuits that achieve a high dynamic range with low basal on-target RNA reduction while minimizing collateral activity in human kidney cells and green monkey cells most frequently used in human virology. MONARCH should bring RNA-guided RNA cutting systems to the forefront, as easily applicable, programmable tools for transcriptome engineering in biotechnological and medical applications.

Fine-tuning gene expression is of great interest for synthetic biotechnological applications. This is particularly true for the genus Streptomyces, which is well-known as a prolific producer of diverse natural products. Currently, there is an increasing demand to develop effective gene induction systems. In this study, bioinformatic analysis revealed a putative rhamnose catabolic pathway in multiple Streptomyces species, and the removal of the pathway in the model organism Streptomyces coelicolor impaired its growth on minimal media with rhamnose as the sole carbon source. To unravel the regulatory mechanism of RhaR, a LacI family transcriptional regulator of the catabolic pathway, electrophoretic mobility shift assays (EMSAs) were performed to identify potential target promoters. Multiple sequence alignments retrieved a consensus sequence of the RhaR operator (rhaO). A synthetic biology-based strategy was then deployed to build rhamnose-inducible regulatory systems, referred to as rhaRS1 and rhaRS2, by assembling the repressor/operator pair RhaR/rhaO with the well-defined constitutive kasO* promoter. Both rhaRS1 and rhaRS2 exhibited a high level of induced reporter activity, with no leaky expression. rhaRS2 has been proven successful for the programmable production of actinorhodin and violacein in Streptomyces. Our study expanded the toolkit of inducible regulatory systems that will be broadly applicable to many other Streptomyces species.

Energy and its dissipation are fundamental to all living systems, including cells. Insufficient abundance of energy carriers─as caused by the additional burden of artificial genetic circuits─shifts a cell's priority to survival, also impairing the functionality of the genetic circuit. Moreover, recent works have shown the importance of energy expenditure in information transmission. Despite living organisms being non-equilibrium systems, non-equilibrium models capable of accounting for energy dissipation and non-equilibrium response curves are not yet employed in genetic design automation (GDA) software. To this end, we introduce Energy Aware Technology Mapping, the automated design of genetic logic circuits with respect to energy efficiency and functionality. The basis for this is an energy aware non-equilibrium steady state model of gene expression, capturing characteristics like energy dissipation─which we link to the entropy production rate─and transcriptional bursting, relevant to eukaryotes as well as prokaryotes. Our evaluation shows that a genetic logic circuit's functional performance and energy efficiency are disjoint optimization goals. For our benchmark, energy efficiency improves by 37.2% on average when comparing to functionally optimized variants. We discover a linear increase in energy expenditure and overall protein expression with the circuit size, where Energy Aware Technology Mapping allows for designing genetic logic circuits with the energetic costs of circuits that are one to two gates smaller. Structural variants improve this further, while results show the Pareto dominance among structures of a single Boolean function. By incorporating energy demand into the design, Energy Aware Technology Mapping enables energy efficiency by design. This extends current GDA tools and complements approaches coping with burden in vivo.

CRISPR gene editing strategies are shaping cell therapies through precise and tunable control over gene expression. However, limitations in safely delivering high quantities of CRISPR machinery demand careful target gene selection to achieve reliable therapeutic effects. Informed target gene selection requires a thorough understanding of the involvement of target genes in gene regulatory networks (GRNs) and thus their impact on cell phenotype. Effective decoding of these complex networks has been achieved using machine learning models, but current techniques are limited to single cell types and focus mainly on transcription factors, limiting their applicability to CRISPR strategies. To address this, we present CRISPR-GEM, a multilayer perceptron (MLP) based synthetic GRN constructed to accurately predict the downstream effects of CRISPR gene editing. First, input and output nodes are identified as differentially expressed genes between defined experimental and target cell/tissue types, respectively. Then, MLP training learns regulatory relationships in a black-box approach allowing accurate prediction of output gene expression using only input gene expression. Finally, CRISPR-mimetic perturbations are made to each input gene individually, and the resulting model predictions are compared to those for the target group to score and assess each input gene as a CRISPR candidate. The top scoring genes provided by CRISPR-GEM therefore best modulate experimental group GRNs to motivate transcriptomic shifts toward a target group phenotype. This machine learning model is the first of its kind for predicting optimal CRISPR target genes and serves as a powerful tool for enhanced CRISPR strategies across a range of cell therapies.

The design and optimization of metabolic pathways, genetic systems, and engineered proteins rely on high-throughput assays to streamline design-build-test-learn cycles. However, assay development is a time-consuming and laborious process. Here, we create a generalizable approach for the tailored optimization of automated cell-free gene expression (CFE)-based workflows, which offers distinct advantages over in vivo assays in reaction flexibility, control, and time to data. Centered around designing highly accurate and precise transfers on the Echo Acoustic Liquid Handler, we introduce pilot assays and validation strategies for each stage of protocol development. We then demonstrate the efficacy of our platform by engineering transcription factor-based biosensors. As a model, we rapidly generate and assay libraries of 127 MerR and 134 CadR transcription factor variants in 3682 unique CFE reactions in less than 48 h to improve limit of detection, selectivity, and dynamic range for mercury and cadmium detection. This was achieved by assessing a panel of ligand conditions for sensitivity (to 0.1, 1, 10 μM Hg and 0, 1, 10, 100 μM Cd for MerR and CadR, respectively) and selectivity (against Ag, As, Cd, Co, Cu, Hg, Ni, Pb, and Zn). We anticipate that our Echo-based, cell-free approach can be used to accelerate multiple design workflows in synthetic biology.

Cancer therapy can be limited by potential side effects, and bacteria-based living cancer therapeutics have gained scientific interest in recent years. However, the full potential of bacteria as therapeutics has yet to be explored due to engineering challenges. In this study, we present a bacterial device designed to specifically target and eliminate breast cancer cells. We have engineered Escherichia coli (E. coli) to bind to HER2 receptors on breast cancer cells while also secreting a toxin, HlyE, which is a pore-forming protein. The binding of E. coli to HER2 is facilitated by a nanobody expressed on the bacteria's surface via the Ag43 autotransporter protein system. Our findings demonstrate that the nanobody efficiently binds to HER2+ cells in vitro, and we have utilized the YebF secretion tag to secrete HlyE and kill the target cancer cells. Overall, our results highlight the potential of our engineered bacteria as an innovative strategy for breast cancer treatment.

Synthesizing viral genomes plays an important role in fundamental virology research and in the development of vaccines and antiviral drugs. Herpes simplex virus type 1 (HSV-1) is a large DNA virus widely used in oncolytic virotherapy. Although de novo synthesis of the HSV-1 genome has been previously reported, the synthetic procedure is still far from efficient, and the synthesized genome contains a vector sequence that may affect its replication and application. In the present study, we developed an efficient vector-free strategy for synthesis and rescue of synthetic HSV-1. In contrast to the conventional method of transfecting mammalian cells with a completely synthesized genome containing a vector, overlapping HSV-1 fragments synthesized by transformation-associated recombination (TAR) in yeast were linearized and cotransfected into mammalian cells to rescue the synthetic virus. Using this strategy, a synthetic virus, F-Syn, comprising the complete genome of the HSV-1 F strain, was generated. The growth curve and electron microscopy of F-Syn confirmed that its replication dynamics and morphogenesis are similar to those of the parental virus. In addition, by combining TAR with in vitro CRISPR/Cas9 editing, an oncolytic virus, F-Syn-O, with deleted viral genes ICP6, ICP34.5, and ICP47 was generated. The antitumor effect of F-Syn-O was tested in vitro. F-Syn-O established a successful infection and induced dose-dependent cytotoxic effects in various human tumor cell lines. These strategies will facilitate convenient and systemic manipulation of HSV-1 genomes and could be further applied to the design and construction of oncolytic herpesviruses.

Stenotrophomonas maltophilia (S. maltophilia, SMA) is a common opportunistic pathogen that poses a serious threat to the food industry and human health. Traditional detection methods for SMA are time-consuming, have low detection rates, require complex and expensive equipment and professional technical personnel for operation, and are unsuitable for on-site detection. Therefore, establishing an efficient on-site detection method has great significance in formulating appropriate treatment strategies and ensuring food safety. In the present study, a rapid one-pot detection method was established for SMA using a combination of Recombinase Polymerase Amplification (RPA) and CRISPR/Cas12a, referred to as ORCas12a-SMA (one-pot RPA-CRISPR/Cas12a platform). In the ORCas12a-SMA detection method, all components were added into a single tube simultaneously to achieve one-pot detection and address the problems of nucleic acid cross-contamination and reduced sensitivity caused by frequent cap opening during stepwise detection. The ORCas12a-SMA method could detect at least 3 × 10° copies·μL^-1 of SMA genomic DNA within 30 min at 37 °C. Additionally, this method exhibited sensitivity compared to the typical two-step RPA-CRISPR/Cas12a method. Overall, the ORCas12a-SMA detection offered the advantages of rapidity, simplicity, high sensitivity and specificity, and decreased need for complex large-scale instrumentation. This assay is the first application of the one-pot platform based on the combination of RPA and CRISPR/Cas12a in SMA detection and is highly suitable for point-of-care testing. It helps reduce losses in the food industry and provides assistance in formulating timely and appropriate antimicrobial treatment plans.