Activation of a diluted E. coli cell-free transcription-translation system within liposomes by hypertonic concentration.
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ABSTRACT:
SUBMITTER: Matsui Y
PROVIDER: S-EPMC8672043 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:Biochemical systems in living cells have their optimum concentration ratio among each constituent element to maintain their functionality. However, in the case of the biochemical system with complex interactions and feedbacks among elements, their activity as a system greatly changes by the concentration shift of the entire system irrespective of the concentration ratio among elements. In this study, by using a transcription-translation (TX-TL) system as the subject, we illustrate the principle of the nonlinear relationship between the system concentration and the activity of the system. Our experiment and simulation showed that shifts of the system concentration of TX-TL by dilution and concentration works as a switch of activity and demonstrated its ability to induce a biochemical system to confer the permeability of small molecules to liposomes. These results contribute to the creation of artificial cells with the switch and provide an insight into the emergence of protocells.
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Project description:Translational regulation was investigated at the genome-scale in Escherichia coli cells. Using the polysome profiling method, the ribosome occupancy (RO) and ribosome density (RD) of different mRNA copies were determined for several hundred mRNAs during the exponential- and stationary-phases, providing the most complete characterization of such regulation in E. coli. Although for most genes, nearly all mRNAs (>90%) were undergoing translation, they were loaded with far fewer than the theoretical maximum number of ribosomes, suggesting translation limitation at the initiation step. Multiple linear regression was used to identify key intrinsic factors involved in the genome-wide regulation of RO and RD (i.e., open reading frame GC%, protein function, and localization). Unexpectedly, mRNA concentration, a factor that depends on cell physiology, was predicted to positively regulate RO and RD during the exponential- and stationary-phases. Using a set of selected genes controlled by an inducible promoter, we confirmed that increasing the mRNA concentration upon transcription induction led to increases in both RO and ribosome load. The fact that this relationship between mRNA concentration and translation parameters was also effective when E. coli cells naturally adapted to carbon source changes demonstrates its physiological relevance. This work demonstrated that translation regulation is positively controlled by transcript availability. This new mechanism contributed to the codirectional regulation of transcription and translation with synergistic effects on gene expression and provided a systemic understanding of E. coli cell function. IMPORTANCE The process of gene expression is divided into translation and transcription. Considerable efforts have been made in bacteria to characterize the mechanisms underlying translational regulation and identify the regulatory factors for particular mRNAs. However, to understand bacterial physiology and adaptation, it is important to elucidate genome-wide translational regulation and examine its coordination with transcriptional regulation. Here, we provided a genome-wide picture of translational regulation in Escherichia coli. For most genes, nearly all mRNA copies were found to undergo translation but were loaded with a low number of ribosomes. We showed that mRNA concentration had a positive effect on translation regulation, linking translational regulation to transcriptional regulation as well as to cell physiology and growth conditions. The codirectional regulation of transcription and translation had synergistic effects on gene expression, contributing to E. coli cell function optimization. This finding could be used in biotechnology to optimize strategies for recombinant protein synthesis.
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Project description:Wheat germ cell-free translation is shown to be an effective method to produce integral membrane proteins in the presence of unilamelar liposomes. In this chapter, we describe the expression vectors, preparation of mRNA, two types of cell-free translation reactions performed in the presence of liposomes, a simple and highly efficient purification of intact proteoliposomes using density gradient ultracentrifugation, and some of the types of characterization studies that are facilitated by this facile preparative approach. The in vitro transfer of newly translated, membrane proteins into liposomes compatible with direct measurements of their catalytic function is contrasted with existing approaches to extract membrane proteins from biological membranes using detergents and subsequently transfer them back to liposomes for functional studies.
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Project description:Cell-free transcription-translation (TXTL) is expanding as a polyvalent experimental platform to engineer biological systems outside living organisms. As the number of TXTL applications and users is rapidly growing, some aspects of this technology could be better characterized to provide a broader description of its basic working mechanisms. In particular, developing simple quantitative biophysical models that grasp the different regimes of in vitro gene expression, using relevant kinetic constants and concentrations of molecular components, remains insufficiently examined. In this work, we present an ODE (Ordinary Differential Equation)-based model of the expression of a reporter gene in an all E. coli TXTL that we apply to a set of regulatory elements spanning several orders of magnitude in strengths, far beyond the T7 standard system used in most of the TXTL platforms. Several key biochemical constants are experimentally determined through fluorescence assays. The robustness of the model is tested against the experimental parameters, and limitations of TXTL resources are described. We establish quantitative references between the performance of E. coli and synthetic promoters and ribosome binding sites. The model and the data should be useful for the TXTL community interested either in gene network engineering or in biomanufacturing beyond the conventional platforms relying on phage transcription.
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Project description:CRISPR-Cas systems offer versatile technologies for genome engineering, yet their implementation has been outpaced by ongoing discoveries of new Cas nucleases and anti-CRISPR proteins. Here, we present the use of E. coli cell-free transcription-translation (TXTL) systems to vastly improve the speed and scalability of CRISPR characterization and validation. TXTL can express active CRISPR machinery from added plasmids and linear DNA, and TXTL can output quantitative dynamics of DNA cleavage and gene repression-all without protein purification or live cells. We used TXTL to measure the dynamics of DNA cleavage and gene repression for single- and multi-effector CRISPR nucleases, predict gene repression strength in E. coli, determine the specificities of 24 diverse anti-CRISPR proteins, and develop a fast and scalable screen for protospacer-adjacent motifs that was successfully applied to five uncharacterized Cpf1 nucleases. These examples underscore how TXTL can facilitate the characterization and application of CRISPR technologies across their many uses.
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Project description:Translational regulation was investigated at the genome scale in Escherichia coli cells. Using the polysome profiling method, the ribosome occupancy (RO) and ribosome density (RD) of different mRNA copies were determined for several hundred mRNAs during the exponential and stationary phases, providing the most complete characterisation of such regulation in E. coli. Although for most genes, nearly all mRNAs (>90%) were undergoing translation, they were loaded with far fewer than the theoretical maximum number of ribosomes, suggesting translation limitation at the initiation step. Multiple linear regression was used to identify key intrinsic factors involved in the genome-wide regulation of RO and RD (i.e., open reading frame GC%, protein function and localisation). Unexpectedly, mRNA concentration, a factor that depends on cell physiology, was predicted to positively regulate RO and RD during the exponential and stationary phases. Using a set of selected genes controlled by an inducible promoter, we confirmed that increasing the mRNA concentration upon transcription induction led to increases in both RO and ribosome load. The fact that this relationship between mRNA concentration and translation parameters was also effective when E. coli cells naturally adapted to carbon source changes demonstrates its physiological relevance. This work demonstrates that translation regulation is positively controlled by transcript availability. This new mechanism contributes to the co-directional regulation of transcription and translation with synergistic effects on gene expression, and provides a systemic understanding of E. coli cell function.
2022-02-23 | GSE191073 | GEO
Project description:We present a protocol to rapidly test DNA binding and cleavage activity by CRISPR nucleases using cell-free transcription-translation (TXTL). Nuclease activity is assessed by adding DNA encoding a nuclease, a guide RNA, and a targeted reporter to a TXTL reaction and by measuring the fluorescence for several h. The reactions, performed in a few microliters, allow for parallel testing of many nucleases and guide RNAs. The protocol includes representative results for (d)Cas9 from Streptococcus pyogenes targeting a GFP reporter gene. For complete information on the generation and use of this protocol, please refer to the paper by Marshall et al. (2018).
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Project description:The exposure of 3T3 cells to a medium made hypertonic by the addition of NaCl induced activation of a heat-shock transcription factor (HSF). This activation, as monitored by gel-mobility-shift assays, occurred within 10 min of hypertonic shock and was dose-dependent in relation to the osmotic strength of the medium up to 0.7 osM. Competition analysis indicated that the effect of hypertonic shock on HSF binding activity was specific. The magnitude of the heat-shock element (HSE)-HSF binding induced by incubating the cells in a 0.7 osM medium was comparable in intensity and time course with that induced by a 44 degrees C heat shock. Following removal of the stressors, the decrease in HSF-HSE binding was more rapid in hypertonicity-shocked than in heat-shocked cells. Treatment of the cells with cycloheximide did not inhibit HSF-HSE binding, indicating that the activation was independent of new protein synthesis. By using a specifically directed polyclonal serum, HSF1 was identified as the transcription factor involved in the hypertonicity-induced activation. HSF was also activated when a membrane-impermeable osmolyte such as sucrose was used to increase the osmolarity of the medium. However, no HSF-HSE binding was observed after addition of glycerol (a freely membrane-permeable osmolyte) in excess. There was a temporal relationship between the hypertonicity-induced volume decrease, the increase in the intracellular K+ concentration and the induction of HSF-HSE binding. In contrast, an increase in the intracellular Na+ concentration was not required to induce HSF-HSE binding. However, unlike the heat-shock response, the activation of HSF by hypertonic shock did not lead to elongation of the RNA transcript of heat-shock protein 70.
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Project description:Transcription and translation are at the heart of metabolism and signal transduction. In this study, we developed an effective biophysical modeling approach to simulate transcription and translation processes. The model, composed of coupled ordinary differential equations, was tested by comparing simulations of two cell free synthetic circuits with experimental measurements generated in this study. First, we considered a simple circuit in which sigma factor 70 induced the expression of green fluorescent protein. This relatively simple case was then followed by a more complex negative feedback circuit in which two control genes were coupled to the expression of a third reporter gene, green fluorescent protein. Many of the model parameters were estimated from previous biophysical studies in the literature, while the remaining unknown model parameters for each circuit were estimated by minimizing the difference between model simulations and messenger RNA (mRNA) and protein measurements generated in this study. In particular, either parameter estimates from published studies were used directly, or characteristic values found in the literature were used to establish feasible ranges for the parameter estimation problem. In order to perform a detailed analysis of the influence of individual model parameters on the expression dynamics of each circuit, global sensitivity analysis was used. Taken together, the effective biophysical modeling approach captured the expression dynamics, including the transcription dynamics, for the two synthetic cell free circuits. While, we considered only two circuits here, this approach could potentially be extended to simulate other genetic circuits in both cell free and whole cell biomolecular applications as the equations governing the regulatory control functions are modular and easily modifiable. The model code, parameters, and analysis scripts are available for download under an MIT software license from the Varnerlab GitHub repository.
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Project description:The inability to predict heterologous gene expression levels precisely hinders our ability to engineer biological systems. Using well-characterized regulatory elements offers a potential solution only if such elements behave predictably when combined. We synthesized 12,563 combinations of common promoters and ribosome binding sites and simultaneously measured DNA, RNA, and protein levels from the entire library. Using a simple model, we found that RNA and protein expression were within twofold of expected levels 80% and 64% of the time, respectively. The large dataset allowed quantitation of global effects, such as translation rate on mRNA stability and mRNA secondary structure on translation rate. However, the worst 5% of constructs deviated from prediction by 13-fold on average, which could hinder large-scale genetic engineering projects. The ease and scale this of approach indicates that rather than relying on prediction or standardization, we can screen synthetic libraries for desired behavior.
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