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

Self-healing laminated composites from prepreg fabrics

Fiber-reinforced polymeric (FRP) composites are eminent engineering materials for structural applications due to their high strength-to-weight ratios. During their service life, FRP composites are often exposed to severe conditions leading to fatigue-induced damage. This damage mode is complex and difficult to detect and repair before eventual catastrophic failure. Continuous fibers are typical reinforcements for FRP composites which provide excellent axial strength and stiffness in the fiber direction. Owing to this directionality, the fatigue-induced damage first creates matrix cracks and fiber/matrix interfacial debonding near the off-axis fibers and evolve into fiber failures and delaminations. Therefore, failure of the FRP composite can be retarded by repairing microscale flaws at the initial stage. Self-healing materials have been developed to autonomously detect and repair the damage. One method of self-healing in a material is to incorporate microcapsules which sequester healing agents. These healing agents are autonomously released and wick into the fracture planes upon rupturing by a damage. Yet, little has been done on capsule-based self-healing FRP composites due to the difficulties in the fabrication process. Existing composite manufacturing processes cause microcapsules to agglomerate or often rupture because of the presence of fiber-reinforcements. As a result, the capsule-based self-healing FRP composites inherently possess a low fiber volume fraction < 40 vol% and agglomerated microcapsules. Another method for creating a self-healing material is to embed microvascular channels which can deliver healing agents from an external reservoir. These pervasive channels can transport large amounts of healing agents to fracture planes with the aid of an external pump, but segregated channels can suffer from blockage. Three-dimensional (3D), inter-connected microvascular networks provide redundant networks improving the flow distribution and minimizing the effects of individual microchannel failure. However, fabricating 3D microvascular networks within a FRP composite is a major challenge for existing rapid and large-scale manufacturing processes. In this thesis, the manufacturing difficulties listed above will be addressed to effortlessly fabricate a high-performance self-healing FRP composite. Microcapsule-containing unidirectional (UD) prepreg fabrics were developed to uniformly distribute the microcapsules at the fiber interstitial spaces. Self-healing FRP composites were fabricated from the microcapsule-containing prepregs and the healing of fatigue-induced damage was demonstrated. In addition, UD prepreg fabric containing sacrificial fibers were developed to create 3D microvascular networks within a FRP composite. This study will drive the adoption of self-healing FRP composites into commercial industries that need high-performance materials with the enhanced reliability. Moreover, the development of multi-functional FRP composites will be facilitated as it guides a solution to uniformly distribute functional fillers without compromising their mechanical integrity

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

Long-term outcomes after revascularization for advanced popliteal artery entrapment syndrome with segmental arterial occlusion

ObjectivesThere are few long-term follow-up studies about the result of revascularization surgery for the treatment of popliteal artery entrapment syndrome (PAES). We performed this retrospective study to analyze the long-term result of revascularization surgery in patients with advanced PAES during the last 16 years.MethodsTwenty-two limbs in 18 consecutive patients with PAES were treated surgically at Seoul National University Hospital between January 1994 and December 2009. The preoperative diagnosis of PAES was made by duplex ultrasonography, three-dimensional computed tomography angiography, magnetic resonance imaging, or conventional angiography. The method of surgical approach was determined by the extent of arterial occlusion in preoperative images.ResultsThe mean age was 31 years old and the majority of patients were men (94%). The chief complaints were claudication in 18 limbs, ischemic rest pain in three limbs, and toe necrosis in one limb. All 22 limbs underwent revascularization for advanced PAES with segmental arterial occlusion. Fourteen limbs underwent musculotendinous section and popliteo-popliteal interposition graft (13 posterior approaches, one medial approach), five femoropopliteal (below-knee) bypasses, one femoro-posterior tibial bypass, and two popliteo-posterior tibial bypasses. All revascularization surgeries were performed with reversed saphenous veins. The overall primary graft patency rates at 1, 3, and 5 years were 80.9%, 74.6%, and 74.6%, respectively. Comparing 5-year graft patency according to the extent of arterial occlusion, patients with occlusion confined to the popliteal artery (n = 14) showed a better patency rate than patients with occlusion extended beyond the popliteal artery (n = 8) with no statistical significance (83.6% vs 53.6%; P = .053). Comparing 5-year graft patency according to the inflow artery, superficial femoral artery inflow (n = 6) showed a worse patency rate than popliteal artery inflow (n = 16) (30.0% vs 85.9%; P = .015).ConclusionIn advanced popliteal entrapment syndrome, longer bypass with superficial femoral artery inflow showed poor long-term graft patency rate. The graft patency rate was excellent in patients whose arterial occlusion was confined to the popliteal artery and treated by popliteal interposition graft with reversed saphenous vein. With these data, we suggest that longer bypass extending beyond the popliteal artery might only be indicated in patients with critical limb ischemia when the extent of disease does not allow short interposition graft

Metabolic engineering with systems biology tools to optimize production of prokaryotic secondary metabolites

This Highlight examines current status of metabolic engineering and systems biology tools deployed for the optimal production of prokaryotic secondary metabolites.</p

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

Metabolic engineering of Escherichia coli for the enhanced production of L-tyrosine

Despite wide applications of L-tyrosine in the market, microbial overproduction of L-tyrosine has been a great challenge due to the complex gene regulations involved in its biosynthetic pathway. To this end, effects of knocking out tyrR on the L-tyrosine production were further explored during the strain development. Also, blocking cellular uptake of L-tyrosine by knocking out tyrosine transporters was examined with respect to L-tyrosine production. Using feedback resistant aroG and tyrA genes (aroG and tyrA hereafter) as initial overexpression targets, which encode 3-deoxy-7-phosphoheptulonate synthase and chorismate mutase/prephenate dehydrogenase, respectively, various combinations of genes were subsequently overexpressed in the Escherichia coli wild-type and tyrR knockout strain, and their effects on the L-tyrosine production were examined. Co-overexpression of aroG , aroL and tyrC, a gene from Zymomonas mobilis functionally similar to tyrA , but insensitive to L-tyrosine, led to the greatest L-tyrosine production regardless of the strains and plasmid constructs examined in this study. The strain BTY2.13 overexpressing the abovementioned three genes together with the removal of the L-tyrosine-specific transporter (tyrP) produced 43.14 g/L of L-tyrosine by fed-batch fermentation using the exponential feeding followed by DO-stat feeding method. This outcome suggested that the tyrR gene knockout was not mandatory for the L-tyrosine overproduction, but the production performance of strains having tyrR appeared to be highly affected by vector systems and feeding methods. With an optimal vector system and a feeding method, tyrP knockout appeared to be more effective in enhancing the L-tyrosine than tyrR knockout. This article is protected by copyright. All rights reserved

Deep learning enables high-quality and high-throughput prediction of enzyme commission numbers

High-quality and high-throughput prediction of enzyme commission (EC) numbers is essential for accurate understanding of enzyme functions, which have many implications in pathologies and industrial biotechnology. Several EC number prediction tools are currently available, but their prediction performance needs to be further improved to precisely and efficiently process an ever-increasing volume of protein sequence data. Here, we report DeepEC, a deep learning-based computational framework that predicts EC numbers for protein sequences with high precision and in a high-throughput manner. DeepEC takes a protein sequence as input and predicts EC numbers as output. DeepEC uses 3 convolutional neural networks (CNNs) as a major engine for the prediction of EC numbers, and also implements homology analysis for EC numbers that cannot be classified by the CNNs. Comparative analyses against 5 representative EC number prediction tools show that DeepEC allows the most precise prediction of EC numbers, and is the fastest and the lightest in terms of the disk space required. Furthermore, DeepEC is the most sensitive in detecting the effects of mutated domains/binding site residues of protein sequences. DeepEC can be used as an independent tool, and also as a third-party software component in combination with other computational platforms that examine metabolic reactions

The genome-scale metabolic network analysis of Zymomonas mobilis ZM4 explains physiological features and suggests ethanol and succinic acid production strategies

<p>Abstract</p> <p>Background</p> <p><it>Zymomonas mobilis </it>ZM4 is a Gram-negative bacterium that can efficiently produce ethanol from various carbon substrates, including glucose, fructose, and sucrose, <it>via </it>the Entner-Doudoroff pathway. However, systems metabolic engineering is required to further enhance its metabolic performance for industrial application. As an important step towards this goal, the genome-scale metabolic model of <it>Z. mobilis </it>is required to systematically analyze <it>in silico </it>the metabolic characteristics of this bacterium under a wide range of genotypic and environmental conditions.</p> <p>Results</p> <p>The genome-scale metabolic model of <it>Z. mobilis </it>ZM4, ZmoMBEL601, was reconstructed based on its annotated genes, literature, physiological and biochemical databases. The metabolic model comprises 579 metabolites and 601 metabolic reactions (571 biochemical conversion and 30 transport reactions), built upon extensive search of existing knowledge. Physiological features of <it>Z. mobilis </it>were then examined using constraints-based flux analysis in detail as follows. First, the physiological changes of <it>Z. mobilis </it>as it shifts from anaerobic to aerobic environments (i.e. aerobic shift) were investigated. Then the intensities of flux-sum, which is the cluster of either all ingoing or outgoing fluxes through a metabolite, and the maximum <it>in silico </it>yields of ethanol for <it>Z. mobilis </it>and <it>Escherichia coli </it>were compared and analyzed. Furthermore, the substrate utilization range of <it>Z. mobilis </it>was expanded to include pentose sugar metabolism by introducing metabolic pathways to allow <it>Z. mobilis </it>to utilize pentose sugars. Finally, double gene knock-out simulations were performed to design a strategy for efficiently producing succinic acid as another example of application of the genome-scale metabolic model of <it>Z. mobilis</it>.</p> <p>Conclusion</p> <p>The genome-scale metabolic model reconstructed in this study was able to successfully represent the metabolic characteristics of <it>Z. mobilis </it>under various conditions as validated by experiments and literature information. This reconstructed metabolic model will allow better understanding of <it>Z. mobilis </it>metabolism and consequently designing metabolic engineering strategies for various biotechnological applications.</p