High energy-charged cell factory for heterologous protein synthesis

Overexpression of gluconeogenic phosphoenolpyruvate carboxykinase (PCK) under glycolytic conditions enables Escherichia coli to maintain a greater intracellular ATP concentration and, consequently, to up-regulate genes for amino acid and nucleotide biosynthesis. To investigate the effect of a high intracellular ATP concentration on heterologous protein synthesis, we studied the expression of a foreign gene product, enhanced green fluorescence protein (eGFP), under control of the T7 promoter in E. coli BL21(DE3) strain overexpressing PCK. This strain was able to maintain twice as much intracellular ATP and to express two times more foreign protein than the control strain. These results indicate that a high energy-charged cell can be beneficial as a protein-synthesizing cell factory. The potential uses of such a cell factory are discussed

Framework for network modularization and Bayesian network analysis to investigate the perturbed metabolic network

Background: Genome-scale metabolic network models have contributed to elucidating biological phenomena, and predicting gene targets to engineer for biotechnological applications. With their increasing importance, their precise network characterization has also been crucial for better understanding of the cellular physiology.Results: We herein introduce a framework for network modularization and Bayesian network analysis (FMB) to investigate organism's metabolism under perturbation. FMB reveals direction of influences among metabolic modules, in which reactions with similar or positively correlated flux variation patterns are clustered, in response to specific perturbation using metabolic flux data. With metabolic flux data calculated by constraints-based flux analysis under both control and perturbation conditions, FMB, in essence, reveals the effects of specific perturbations on the biological system through network modularization and Bayesian network analysis at metabolic modular level. As a demonstration, this framework was applied to the genetically perturbed Escherichia coli metabolism, which is a lpdA gene knockout mutant, using its genome-scale metabolic network model.Conclusions: After all, it provides alternative scenarios of metabolic flux distributions in response to the perturbation, which are complementary to the data obtained from conventionally available genome-wide high-throughput techniques or metabolic flux analysis

New time-scale criteria for model simplification of bio-reaction systems

<p>Abstract</p> <p>Background</p> <p>Quasi-steady state approximation (QSSA) based on time-scale analysis is known to be an effective method for simplifying metabolic reaction system, but the conventional analysis becomes time-consuming and tedious when the system is large. Although there are automatic methods, they are based on eigenvalue calculations of the Jacobian matrix and on linear transformations, which have a high computation cost. A more efficient estimation approach is necessary for complex systems.</p> <p>Results</p> <p>This work derived new time-scale factor by focusing on the problem structure. By mathematically reasoning the balancing behavior of fast species, new time-scale criteria were derived with a simple expression that uses the Jacobian matrix directly. The algorithm requires no linear transformation or decomposition of the Jacobian matrix, which has been an essential part for previous automatic time-scaling methods. Furthermore, the proposed scale factor is estimated locally. Therefore, an iterative procedure was also developed to find the possible multiple boundary layers and to derive an appropriate reduced model.</p> <p>Conclusion</p> <p>By successive calculation of the newly derived time-scale criteria, it was possible to detect multiple boundary layers of full ordinary differential equation (ODE) models. Besides, the iterative procedure could derive the appropriate reduced differential algebraic equation (DAE) model with consistent initial values, which was tested with simple examples and a practical example.</p

CRISPy-web:An online resource to design sgRNAs for CRISPR applications

CRISPR/Cas9-based genome editing has been one of the major achievements of molecular biology, allowing the targeted engineering of a wide range of genomes. The system originally evolved in prokaryotes as an adaptive immune system against bacteriophage infections. It now sees widespread application in genome engineering workflows, especially using the Streptococcus pyogenes endonuclease Cas9. To utilize Cas9, so-called single guide RNAs (sgRNAs) need to be designed for each target gene. While there are many tools available to design sgRNAs for the popular model organisms, only few tools that allow designing sgRNAs for non-model organisms exist. Here, we present CRISPy-web (http://crispy.secondarymetabolites.org/), an easy to use web tool based on CRISPy to design sgRNAs for any user-provided microbial genome. CRISPy-web allows researchers to interactively select a region of their genome of interest to scan for possible sgRNAs. After checks for potential off-target matches, the resulting sgRNA sequences are displayed graphically and can be exported to text files. All steps and information are accessible from a web browser without the requirement to install and use command line scripts

Genome-scale metabolic model of the fission yeast Schizosaccharomyces pombe and the reconciliation of in silico/in vivo mutant growth

<p>Abstract</p> <p>Background</p> <p>Over the last decade, the genome-scale metabolic models have been playing increasingly important roles in elucidating metabolic characteristics of biological systems for a wide range of applications including, but not limited to, system-wide identification of drug targets and production of high value biochemical compounds. However, these genome-scale metabolic models must be able to first predict known <it>in vivo</it> phenotypes before it is applied towards these applications with high confidence. One benchmark for measuring the <it>in silico</it> capability in predicting <it>in vivo</it> phenotypes is the use of single-gene mutant libraries to measure the accuracy of knockout simulations in predicting mutant growth phenotypes.</p> <p>Results</p> <p>Here we employed a systematic and iterative process, designated as Reconciling <it>In silico/in vivo</it> mutaNt Growth (RING), to settle discrepancies between <it>in silico</it> prediction and <it>in vivo</it> observations to a newly reconstructed genome-scale metabolic model of the fission yeast, <it>Schizosaccharomyces pombe</it>, SpoMBEL1693. The predictive capabilities of the genome-scale metabolic model in predicting single-gene mutant growth phenotypes were measured against the single-gene mutant library of <it>S. pombe</it>. The use of RING resulted in improving the overall predictive capability of SpoMBEL1693 by 21.5%, from 61.2% to 82.7% (92.5% of the negative predictions matched the observed growth phenotype and 79.7% the positive predictions matched the observed growth phenotype).</p> <p>Conclusion</p> <p>This study presents validation and refinement of a newly reconstructed metabolic model of the yeast <it>S. pombe</it>, through improving the metabolic model’s predictive capabilities by reconciling the <it>in silico</it> predicted growth phenotypes of single-gene knockout mutants, with experimental <it>in vivo</it> growth data.</p

Proteome-Level Responses of Escherichia coli to Long-Chain Fatty Acids and Use of Fatty Acid Inducible Promoter in Protein Production

In Escherichia coli, a long-chain acyl-CoA is a regulatory signal that modulates gene expression through its binding to a transcription factor FadR. In this study, comparative proteomic analysis of E. coli in the presence of glucose and oleic acid was performed to understand cell physiology in response to oleic acid. Among total of 52 proteins showing altered expression levels with oleic acid presence, 9 proteins including AldA, Cdd, FadA, FadB, FadL, MalE, RbsB, Udp, and YccU were newly synthesized. Among the genes that were induced by oleic acid, the promoter of the aldA gene was used for the production of a green fluorescent protein (GFP). Analysis of fluorescence intensities and confocal microscopic images revealed that soluble GFP was highly expressed under the control of the aldA promoter. These results suggest that proteomics is playing an important role not only in biological research but also in various biotechnological applications