Synthetic biology (Oxford, England)最新文献

Cell-free expression (CFE) systems are emerging as a powerful tool in synthetic biology, with diverse applications from prototyping genetic circuits to serving as a platform for point-of-care biosensors. When multiple genes need to be expressed in the same CFE reaction, their DNA templates (often added as plasmids) are generally assumed to behave independently of each other, with neither affecting the other's expression. However, recent work in Escherichia coli CFE systems shows that multiple aspects of these templates can lead to antagonistic or synergistic interactions in expression levels of individual genes, a phenomenon referred to as plasmid crosstalk. Plasmid crosstalk can confound efforts for precise engineering of genetic circuits and even give rise to misleading observations about circuit function. Unfortunately, current mathematical and computational models are incapable of reproducing critical aspects of plasmid crosstalk. To address this gap, we created an ordinary differential equation model incorporating mechanisms to account for competition for transcription, translation, and degradation resources, as well as toxic molecule build-up. This model can recapitulate the predominant observed phenomena of plasmid crosstalk. Simulation results and subsequent experimental validation provided insights into the different sources of burden and interactions in CFE systems, including that translation is negatively impacted by macromolecular crowding caused by possibly both transcription and translation. This model thus enables deeper understanding of CFE systems and could serve as a useful tool for future CFE application design.

Sophisticated genetic engineering tasks such as protein domain grafting and multi-gene fusions are hampered by the lack of suitable vector backbones. In particular, many restriction sites are in the backbone outside the polylinker region (multiple cloning site; MCS) and thus unavailable for use, and the overall length of a plasmid correlates with poorer ligation efficiency. To address this need, we describe the design and validation of a collection of six minimal integrating or centromeric shuttle vectors for Saccharomyces cerevisiae, a widely used model organism in synthetic biology. We constructed the plasmids using de novo gene synthesis and consisting only of a yeast selection marker (HIS3, LEU2, TRP1, URA3, KanMX, or natMX6), a bacterial selection marker (ampicillin resistance), an origin of replication, and the MCS flanked by M13 forward and reverse sequences. We used truncated variants of these elements where available and eliminated all other sequences typically found in plasmids. The MCS consists of ten unique restriction sites. To our knowledge, at sizes ranging from ~2.6 to 3.5 kb, these are the smallest shuttle vectors described for yeast. Further, we removed common restriction sites in the open reading frames and terminators, freeing up ~30 cut sites in each plasmid. We named our pLS series in accordance with the well-known pRS vectors, which are on average 63% larger: pLS400, pLS410 (KanMX); pLS403, pLS413 (HIS3); pLS404, pLS414 (TRP1); pLS405, pLS415 (LEU2); pLS406, pLS416 (URA3); and pLS408, pLS418 (natMX6). This resource substantially simplifies advanced synthetic biology engineering in S. cerevisiae.

The β-proteobacterial species Curvibacter sp. AEP1-3 is a model organism for the study of symbiotic interactions as it is the most abundant colonizer of Hydra vulgaris. Yet, genetic tools for Curvibacter are still in their infancy; few promoters have been characterized so far. Here, we employ an oligonucleotide-based strategy to develop novel expression systems Curvibacter. Potential promoters were systematically mined from the genome in silico. The sequences were cloned as a mixed library into a mCherry reporter vector and positive candidates were selected by Flow Cytometry to be further analysed through plate reader measurements. From 500 candidate sequences, 25 were identified as active promoters of varying expression strength levels. Plate reader measurements revealed unique activity profiles for these sequences across growth phases. The expression levels of these promoters ranged over two orders of magnitudes and showed distinct temporal expression dynamics over the growth phases: while three sequences showed higher expression levels in the exponential phase, we found 12 sequences saturating expression during stationary phase and 10 that showed little discrimination between growth phases. From our library, promoters of the genes dnaK, rpsL and an acyl-homoserine-lactone (AHL) synthase stood out as the most interesting candidates fit for a variety of applications. We identified enriched transcription factor binding motifs among the sorted 33 sequences and genes encoding for homologs of these transcription factors in close proximity to the identified motifs. In this work, we show the value of employing comprehensive high-throughput strategies to establish expression systems for novel model organisms.

Heparosan is a natural polymer with unique chemical and biological properties, that holds great promise for biomedical applications. The molecular weight (Mw) and polydispersion index (PDI) are critical factors influencing the performance of heparosan-based materials. Achieving precise control over the synthesis process to consistently produce heparosan with low Mw and low PDI can be challenging, as it requires tight regulation of reaction conditions, enzyme activity, and precursor concentrations. We propose a novel approach utilizing synthetic biology principles to precisely control heparosan biosynthesis in bacteria. Our strategy involves designing a biomolecular controller that can regulate the expression of genes involved in heparosan biosynthesis. This controller is activated by biosensors that detect heparosan precursors, allowing for fine-tuned control of the polymerization process. Through this approach, we foresee the implementation of this synthetic device, demonstrating the potential to produce low Mw and low PDI heparosan in the probiotic E. coli Nissle 1917 as a biosafe and biosecure biofactory. This study represents a significant advancement in the field of heparosan production, offering new opportunities for the development and manufacturing of biomaterials with tailored properties for diverse biomedical applications.

Plant synthetic biologists have been working to adapt the CRISPRa and CRISPRi promoter regulation methods for applications such as improving crops or installing other valuable pathways. With other organisms, strong transcriptional control has typically required multiple gRNA target sites, which poses a critical engineering choice between heterogeneous sites, which allow each gRNA to target existing locations in a promoter, and identical sites, which typically require modification of the promoter. Here, we investigate the consequences of this choice for CRISPRi plant promoter regulation via simulation-based analysis, using model parameters based on single gRNA regulation and constitutive promoters in Nicotiana benthamiana and Arabidopsis thaliana. Using models of 2-6 gRNA target sites to compare heterogeneous versus identical sites for tunability, sensitivity to parameter values, and sensitivity to cell-to-cell variation, we find that identical gRNA target sites are predicted to yield far more effective transcriptional repression than heterogeneous sites.

Engineering enzymes to degrade solid substrates, such as crystalline cellulose from paper sludge or microplastics in sewage sludge, presents challenges for high-throughput screening (HTS), as solid substrates are not readily accessible in cell-based biosensor systems. To address this challenge, we developed a cell-free cellobiose-detectable biosensor (CB-biosensor) for rapid characterization of cellobiohydrolase (CBH) activity, enabling direct detection of hydrolysis products without cellular constraints. The CB-biosensor demonstrates higher sensitivity than conventional assays and distinguishes between CBH subtypes (CBHI and CBHII) based on their modes of action. Integration with the Echo 525 liquid handler enables precise and reproducible sample processing, with fluorescence signals from automated preparations comparable to manual experiments. Furthermore, assay volumes can be reduced to just a few microlitres-impractical with manual methods. This cell-free CB-biosensor with Echo 525 minimizes reagent consumption, accelerates testing, and facilitates reliable large-scale screening. These findings highlight its potential to overcome current HTS limitations, advancing enzyme screening and accelerating the Design-Build-Test-Learn cycle for sustainable biomanufacturing.

OT-Mation is an open-source Python script designed to automate the programming of OT-2 liquid-handling robots, making combinatorial experiments more accessible to researchers. By parsing user-defined CSV files containing information on labware, reagents, pipettes, and experimental design, OT-Mation generates a bespoke Python script compatible with the OT-2 system. OT-Mation enhances reproducibility, reduces human error, and streamlines workflows, making it a valuable addition to any laboratory utilizing OT-2 robotics for liquid handling. While OT-Mation can be used for setting up any type of experiment on the OT-2, its real utility lies in making the connection between multifactorial experimental design software outputs (i.e. design of experiments arrays) and liquid-handling robot executable code. As such, OT-Mation helps bridge the gap between code-based flexibility and user-friendly operation, allowing researchers with limited programming skills to design and execute complex experiments efficiently. Graphical Abstract.

DNA has emerged as a promising material to address growing data storage demands. We recently demonstrated a structure-based DNA data storage approach where DNA probes are spatially oriented on the surface of DNA origami and decoded using DNA-PAINT. In this approach, larger origami structures could improve the efficiency of reading and writing data. However, larger origami require long single-stranded DNA scaffolds that are not commonly available. Here, we report the engineering of a novel longer DNA scaffold designed to produce a larger rectangle origami needed to expand the origami-based digital nucleic acid memory (dNAM) approach. We confirmed that this scaffold self-assembled into the correct origami platform and correctly positioned DNA data strands using atomic force microscopy and DNA-PAINT super-resolution microscopy. This larger structure enables a 67% increase in the number of data points per origami and will support efforts to efficiently scale up origami-based dNAM.

Glycolaldehyde (GA), the smallest sugar, has significant potential as a biomass-derived platform chemical and is a key metabolite in several synthetic pathways for one-carbon metabolism and new-to-nature photorespiration. This study introduces two metabolic schemes for engineering Escherichia coli into GA biosensors. Through creating GA-dependent auxotrophies, we link growth of these strains to GA-dependent biosynthesis of the essential vitamin pyridoxal-5-phosphate, and 2-ketoglutarate, respectively. We characterized and optimized these strains for the quantification of externally added GA from 2 µM to 1.5 mM. We also demonstrate the capability of these strains to detect GA that is produced intracellularly through different metabolic routes and from different substrates such as xylose, ethylene glycol, and glycolate. Our biosensors offer complementary sensitivities and features, opening up different applications in metabolic engineering and synthetic biology, which we demonstrate in a proof-of-principle by providing the first in vivo demonstration of the reduction of glycolate to GA by a new-to-nature route using engineered enzymes.

Loop-mediated isothermal amplification (LAMP), a DNA amplification technique under isothermal conditions, provides the important benefits of high sensitivity, specificity, rapidity, and simplicity. Maximizing LAMP features necessitates the design of a complex LAMP primer set (LPS) consisting of four primers for six regions of a given target DNA. Furthermore, the LPS of a given target DNA is designed with LPS design support software such as Primer Explorer. However, even if the design is completed, we still must do many in vitro experiments and evaluations. Consequently, designing LPS often fails to achieve high performance, including efficient amplification. For this study, we examined in silico LAMP: a generalized linear model to predict DNA amplification from LPS. Using logistic regression with elastic net regularization, we identified factors that strongly affect LPS design. These factors, combined with domain knowledge for LPS design, led to the creation of LAMP kernel variables that are highly essential for high LAMP reaction. In silico LAMP, constructed using logistic regression with LAMP kernel variables, allows classification and performance prediction of LPS with an area under the curve of 0.86. These results suggest that a high LAMP reaction can be predicted using LAMP kernel variables and generalized linear regression model. Moreover, an LPS with high performance can be constructed without in vitro experimentation.