Synthetic biology (Oxford, England)最新文献

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.

Recent advances in molecular and cell biology and imaging have unprecedentedly enabled multiscale structure-functional studies of entire metabolic pathways from atomic to micrometer resolution and the visualization of macromolecular complexes in situ, especially if these molecules are expressed with appropriately engineered and easily detectable tags. However, genome editing in eukaryotic cells is challenging when generating stable cell lines loaded with large DNA cargoes. To address this limitation, here, we have conceived biGMamAct, a system that allows the straightforward assembly of a multitude of genetic modules and their subsequent integration in the genome at the ACTB locus with high efficacy, through standardized cloning steps. Our system comprises a set of modular plasmids for mammalian expression, which can be efficiently docked into the genome in tandem with a validated Cas9/sgRNA pair through homologous-independent targeted insertion. As a proof of concept, we have generated a stable cell line loaded with an 18.3-kilobase-long DNA cargo to express six fluorescently tagged proteins and simultaneously visualize five different subcellular compartments. Our protocol leads from the in silico design to the genetic and functional characterization of single clones within 6 weeks and can be implemented by any researcher with familiarity with molecular biology and access to mammalian cell culturing infrastructure.

Many studies characterize transcription factors and other regulatory elements to control gene expression in recombinant systems. However, most lack a formal approach to analyse the inherent and context-specific variations of these regulatory components. This study addresses this gap by establishing a formal framework from which convenient methods are inferred to characterize regulatory circuits. We modelled the bacterial cell as a collection of proteome fractions. Deriving the time-dependent proteome fraction, we obtained a general theorem that describes its change as a function of its expression fraction, a specific portion of the total biosynthesis flux of the cell. Formal deduction reveals that when the proteome fraction reaches a maximum, it becomes equivalent to its expression fraction. This equation enables the reliable measurement of the expression fraction through direct protein quantification. In addition, the experimental data demonstrate a linear correlation between protein production rate and specific growth rate over a significant time period. This suggests a constant expression fraction within this window. For an Isopropyl β- d-1-thiogalactopyranoside (IPTG) biosensor, in five cellular contexts, expression fractions determined by the maximum method and the slope method produced strikingly similar dose-response parameters when independently fit to a Hill function. Furthermore, by analysing two more biosensors, for mercury and cumate detection, we demonstrate that the slope method can be applied effectively to various systems. Therefore, the concepts presented here provide convenient methods for obtaining dose-response parameters, clearly defining the time interval of their validity and offering a framework for interpreting typical biosensor outputs in terms of bacterial physiology. Graphical Abstract Nutrients, transformed by the action of the Nutrient Fixators (purple arrow), are used at a rate of ρ for Protein biosynthesis. The total rate ρ is multiplied by expression fractions f_R, f_C, f_H, and f_Q to obtain the biosynthesis rate (black arrows) of each proteome fraction Φ_R, Φ_C, Φ_H, Φ_Q, respectively. In a graph of Growth rate versus Proteome Fraction Production Rate, a linear function (green lines) can be observed, and its slope is equal to the expression fraction at each condition.

In this study, we introduce a new in vitro method for oligonucleotide fragment assembly. Unlike polymerase chain assembly and ligase chain assembly that rely on short, highly purified oligonucleotides, our method, named Splynthesis, uses a one-tube, splint-driven assembly reaction. Splynthesis connects standard-desalted "contig" oligos (∼150 nt in length) via shorter "splint" oligos harboring 5' and 3' blocking modifications to prevent off-target ligation and amplification events. We demonstrate the Splynthesis method to assemble a 741-bp gene fragment. We verify the assembled polymerase chain reaction product using standard molecular biology techniques, as well as long-read Oxford Nanopore sequencing, and confirm that the product is cloneable via molecular means, as well as Sanger sequencing. This approach is applicable for synthetic biology, directed evolution, functional protein assays, and potentially even splint-based ligase chain reaction assays.

Foundational techniques in molecular biology-such as cloning genes, tagging biomolecules for purification or identification, and overexpressing recombinant proteins-rely on introducing non-native or synthetic DNA sequences into organisms. These sequences may be recognized by the transcription and translation machinery in their new context in unintended ways. The cryptic gene expression that sometimes results has been shown to produce genetic instability and mask experimental signals. Computational tools have been developed to predict individual types of gene expression elements, but it can be difficult for researchers to contextualize their collective output. Here, we introduce CryptKeeper, a software pipeline that visualizes predictions of Escherichia coli gene expression signals and estimates the translational burden possible from a DNA sequence. We investigate several published examples where cryptic gene expression in E. coli interfered with experiments. CryptKeeper accurately postdicts unwanted gene expression from both eukaryotic virus infectious clones and individual proteins that led to genetic instability. It also identifies off-target gene expression elements that resulted in truncations that confounded protein purification. Incorporating negative design using CryptKeeper into reverse genetics and synthetic biology workflows can help to mitigate cloning challenges and avoid unexplained failures and complications that arise from unintentional gene expression.

Developments in bioengineering and nanotechnology have ignited the research on biological and molecular communication systems. Despite potential benefits, engineering communication systems to carry data signals using biological messenger molecules and engineered cells is challenging. Diffusing molecules may fall behind their schedule to arrive at the receiver, interfering with symbols of subsequent time slots and distorting the signal. Existing theoretical molecular communication models often focus solely on the characteristics of a communication channel and fail to provide an end-to-end system response since they assume a simple thresholding process for a receiver cell and overlook how the receiver can detect the incoming distorted molecular signal. In this paper, we present a model-based and computational framework called BioRxToolbox for designing diffusion-based and end-to-end molecular communication systems coupled with synthetic genetic circuits. We describe a novel framework to encode information as a sequence of bits, each transmitted from the sender as a burst of molecules, control cellular behavior at the receiver, and minimize cellular signal interference by employing equalization techniques from communication theory. This approach allows the encoding and decoding of data bits efficiently using two different types of molecules that act as the data carrier and the antagonist to cancel out the heavy tail of the former. Here, BioRxToolbox is demonstrated using a biological design and computational simulations for various communication scenarios. This toolbox facilitates automating the choice of communication parameters and identifying the best communication scenarios that can produce efficient cellular signals.

Golden Gate cloning has become one of the most important DNA assembly strategies. The construction of standardized and reusable part libraries, their assembly into transcription units, and the subsequent assembly of multigene constructs is highly reliable and sustainable. Researchers can quickly construct derivatives of their assemblies or entire pathways, and importantly, the standardization of Golden Gate assemblies is compatible with laboratory automation. Most Golden Gate strategies rely on 4-nt overhangs generated by commonly used Type IIS enzymes. However, reduction to 3-nt overhangs allows the use of codons as fusion sites and reduces potential scar sequences. This is particularly important when studying biological functions, as additional nucleotides may alter the structure or stability of the transcribed RNA. To address this issue we use SapI, a Type IIS enzyme generating three nucleotide overhangs, for transcription unit assembly, allowing for codon-based fusion in coding sequences. We created a corresponding plasmid toolbox for basic part generation and transcription unit assembly, a workflow we term as In-Cloning. In-Cloning is downstream compatible with the Modular Cloning standard developed by Sylvestre Marillonnet's group for standardized assembly of multigene constructs. However, the multigene construct plasmids may not be compatible for use with the model organism of choice. Therefore, we have developed a workflow called Out-Cloning to rapidly generate Golden Gate acceptor plasmids. Out-Cloning uses standardized plasmid parts that are assembled into Golden Gate acceptor plasmids using flexible linkers. This allows the systematic construction of acceptor plasmids needed to transfer assembled DNA into the organism of interest.