Synthetic biology (Oxford, England)最新文献

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.

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.

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.

In vitro metabolic systems allow the reconstitution of natural and new-to-nature pathways outside of their cellular context and are of increasing interest in bottom-up synthetic biology, cell-free manufacturing, and metabolic engineering. Yet, the analysis of the activity of such in vitro networks is very often restricted by time- and cost-intensive methods. To overcome these limitations, we sought to develop an in vitro transcription (IVT)-based biosensing workflow that is compatible with the complex conditions of in vitro metabolism, such as the crotonyl-CoA/ethylmalonyl-CoA/hydroxybutyryl-CoA (CETCH) cycle, a 27-component in vitro metabolic system that converts CO₂ into glycolate. As proof of concept, we constructed a novel glycolate sensor module that is based on the transcriptional repressor GlcR from Paracoccus denitrificans and established an IVT biosensing workflow that allows us to quantify glycolate from CETCH samples in the micromolar to millimolar range. We investigate the influence of 13 (shared) cofactors between the two in vitro systems to show that Mg²⁺, adenosine triphosphate , and other phosphorylated metabolites are critical for robust signal output. Our optimized IVT biosensor correlates well with liquid chromatography-mass spectrometry-based glycolate quantification of CETCH samples, with one or multiple components varying (linear correlation 0.94-0.98), but notably at ∼10-fold lowered cost and ∼10 times faster turnover time. Our results demonstrate the potential and challenges of IVT-based systems to quantify and prototype the activity of complex reaction cascades and in vitro metabolic networks.

Bacteriophages are promising alternatives to traditional antimicrobial treatment of bacterial infections. To further increase the potential of phages, efficient engineering methods are needed. This study investigates an approach to phage engineering based on phage rebooting and compares selected methods of assembly and rebooting of phage genomes. GG assembly of phage genomes and subsequent rebooting by cell-free transcription-translation reactions yielded the most efficient phage engineering and allowed production of a proof-of-concept T7 phage library of 1.8 × 10⁷ phages. We obtained 7.5 × 10⁶ different phages, demonstrating generation of large and diverse libraries suitable for high-throughput screening of mutant phenotypes. Implementing versatile and high-throughput phage engineering methods allows vastly accelerated and improved phage engineering, bringing us closer to applying effective phages to treat infections in the clinic.

Data science is playing an increasingly important role in the design and analysis of engineered biology. This has been fueled by the development of high-throughput methods like massively parallel reporter assays, data-rich microscopy techniques, computational protein structure prediction and design, and the development of whole-cell models able to generate huge volumes of data. Although the ability to apply data-centric analyses in these contexts is appealing and increasingly simple to do, it comes with potential risks. For example, how might biases in the underlying data affect the validity of a result and what might the environmental impact of large-scale data analyses be? Here, we present a community-developed framework for assessing data hazards to help address these concerns and demonstrate its application to two synthetic biology case studies. We show the diversity of considerations that arise in common types of bioengineering projects and provide some guidelines and mitigating steps. Understanding potential issues and dangers when working with data and proactively addressing them will be essential for ensuring the appropriate use of emerging data-intensive AI methods and help increase the trustworthiness of their applications in synthetic biology.