Gene knockout experiments are frequently performed for both fundamental and applied biological research. We developed an integration helper plasmid-based knockout system for more efficient and rapid engineering of Escherichia coli. The integration helper plasmid, pCW611, contains two recombinases which are expressed in reverse direction by two independent inducible systems. One is Red recombinase under the control of arabinose inducible system to induce recombination event by using the linear gene knockout DNA fragment while the other is Cre recombinase which is controlled by IPTG inducible system to obtain marker-less mutant strains. The time and effort required can be reduced by using this system because iterative transformation and curing steps are not required. We could delete one target gene in 3 days by using pCW611. To verify the usefulness of this system, the deletion experiments were performed for knocking out four target genes individually (adhE, sfcA, frdABCD, and ackA) and two genes simultaneously for two cases (adhE-aspA and sfcA-aspA). Also, sequential deletion of four target genes (fumB, iclR, fumA, and fumC) was successfully performed to make fumaric acid producing strain. This rapid and efficient gene manipulation system successfully developed and validated should be useful for the metabolic engineering of E. coli.

Redox potential (ORP) plays a pivotal role in yeast viability and ethanol production during very-high-gravity (VHG) ethanol fermentation. In order to identify the correlation between redox potential profiles and gene expression patterns, global gene expression of Saccharomyces cerevisiae was thus investigated. Results indicated that significant changes in gene expression occurred at the periods of 0-6h and 30-36h, respectively, and these changes were mainly related to carbohydrate metabolism and stress response, respectively. Although CDC19 was down-regulated, expression of PYK2, PDC6 and ADH2 correlated inversely with ORP. Meanwhile, expression of GPD1 decreased due to the depletion of dissolved oxygen in the fermentation broth, but expression of GPD2 correlated with ORP. As for genes encoding HSPs, expression profiles were characterized by uphill, downhill, valley and plateau types, due to their different functions in stress response. These results highlighted the role of ORP in modulating yeast physiology and metabolism under VHG conditions.

Polymeric nanoparticles have emerged as a promising approach for drug delivery systems (DDS). The chitosan/sodium alginate polyelectrolyte complex nanoparticles (CS/SAL NPs) were prepared via a simple and mild ionic-gelation method by addition of a chitosan (CS) solution into a sodium alginate (SAL) solution. Effects of molecular weight of CS added and the alginate:chitosan mass ratio on the formation of the polyelectrolyte complex nanoparticles were investigated. The well-defined CS/SAL NPs with near-monodisperse particle size of about 160 nm exhibited pH stable structure, pH responsive properties with negatively or positively charged surface. The so-called “electrostatic sponge” structure of the polyelectrolyte complex nanoparticles could enhance their drug-loading capacity towards the different sign-charged model drug molecules, and favor the controlled release. It was also found that the drug-loading capacity was influenced by the nature of the drugs and the drug-loading media, while the drug-releasing was affected by the solubility of the drugs in the drug-releasing media. The biocompatibility and biodegradability of the polyelectrolytes in the polyelectrolyte complex nanoparticles could be remained due to the ionic interaction. These results indicated that the CS/SAL polyelectrolyte complex nanoparticles can be suggested as a potentially useful technique for pH-stimuli responsive drug delivery systems.

Due to increasing demand for natural sources of both polyunsaturated fatty acids (PUFAs) and beta-carotene, twenty eight Zygomycetes fungal soil isolates were screened for their potential to synthesize these biologically active compounds. Although all fungi produced C18 PUFAs, only nine strains were described to also form beta-carotene. While Actinomucor elegans CCF 3218 was found as the best producer of gamma-linolenic acid (GLA) (251 mg/L), Umbelopsis isabellina CCF 2412 was considered as the most valuable fungus for dual production of both GLA (217 mg/L) and beta-carotene (40.7 mg/L). Calculated ratio of formed PUFAs provided new insight into activities of individual fatty acid desaturases that are involved to biosynthetic pathways for various types of PUFAs. The maximal activity of delta-9 desaturase was accompanied by high accumulation of storage lipids in fungal cells. Contrarily, maximal activity of delta-15 desaturase was found in strains synthesizing low amounts of oleic acid due to diminished delta-9 desaturase. Activities of delta-6 desaturase showed competition for fatty acids engaged in n3, n6 and n9 biosynthetic pathways. Such knowledge about fatty acid desaturase activities provides new challenges for aimed regulation of biotechnological production of PUFAs by Zygomecetes fungi.

Characteristics of many tumor types are the reprogramming of metabolism and the occurrence of regional hypoxia. In this work we investigated the hypothesis that metabolic reprogramming in combination with metabolic zonation of cellular energy metabolism are important factors to promote the growth capacity of solid tumors. A tissue-scale model of the two main ATP delivering pathways, glycolysis (GLY) and oxidative phosphorylation (OXP) was used to simulate the energy metabolism within solid tumors under various metabolic strategies. Remarkably, despite the high diversity in the usage of glucose, lactate and oxygen in various spatial regions, the tumor as a whole clearly displays the hallmark of the so-called Warburg effect, i.e. a high rate of glucose consumption and lactate production in the presence of sufficiently high levels of oxygen. Our simulations suggest that an increase in GLY capacity and concomitant decrease in OXP capacity from the periphery towards the centre of the tumor improves the availability of oxygen to pericentral tumor cells. The found relationship between the regional oxygen level and the relative share of GLY and OXP capacities supports the view of metabolite availability to function as key regulators of tumor energy metabolism.

The effect of raw materials on product yield and quality in bio-manufacturing is considerable and often poorly understood. Here we describe the capabilities of NIR (near-infrared) and two dimensional (2D)-fluorescence spectroscopies in detecting chemical changes over time in two media components (a basal and a feed media) used in Chinese hamster ovary (CHO) cell cultivations for monoclonal antibodies (Mabs) production. For the basal medium both spectroscopies were able to detect compositional changes over storage time. For the feed medium NIR spectroscopy was more effective in detecting those changes. The impact of storage time in process performance was evaluated by using aged media components in Mab cultivations. The study suggests that basal media aging results in a decrease of the integral of viable cells (IVC) i.e. cell growth over time, while product titer is not significantly affected. Feed media is less sensitive to storage and therefore it is not possible to establish a correlation between the age of the media and cell culture performance. Results obtained provide a basis on which to further improve raw-material quality assessment using vibrational (i.e. NIR) and optical (i.e. 2D-fluorescence) spectroscopic methods leading to the development of more consistent bioprocesses in terms of conditions at cell harvest.

The ensemble modeling (EM) approach has shown promise in capturing kinetic and regulatory effects in the modeling of metabolic networks. Efficacy of the EM procedure relies on the identification of model parameterizations that adequately describe all observed metabolic phenotypes upon perturbation. In this study we propose an optimization-based algorithm for the systematic identification of genetic/enzyme perturbations to maximally reduce the number of models retained in the ensemble after each round of model screening. The key premise here is to design perturbations that will maximally scatter the predicted steady-state fluxes over the ensemble parameterizations. We demonstrate the applicability of this procedure for an E. coli metabolic model of central metabolism by successively identifying single, double and triple enzyme perturbations that cause the maximum degree of flux separation between models in the ensemble. Results revealed that optimal perturbations are not always located close to reaction(s) whose fluxes are measured, especially when multiple perturbations are considered. In addition, there appears to be a maximum number of simultaneous perturbations beyond which no appreciable increase in the divergence of flux predictions is achieved. Overall, this study provides a systematic way of optimally designing genetic perturbations for populating the ensemble of models with relevant model parameterizations.

The identification of optimal expression conditions for state of the art production of pharmaceutical proteins is a very time-consuming and expensive process. In this report an approach for rapid and reproducible optimization of protein expression in an in-house designed small scale BIOSTAT® multi-bioreactor plant is described. A newly developed BioPAT® MFCS/win DoE module (Sartorius Stedim Systems, Germany) connects the process control system MFCS/win and the DoE software MODDE® (Umetrics AB, Sweden) and enables therefore the implementation of fully automated optimization procedures. As a development example a commercial Pichia pastoris strain KM71H has been transformed for the expression of potential Malaria vaccines. This approach has allowed a doubling of intact protein secretion productivity due to DoE-optimization procedure compared to first cultivation results. In a next step, robustness regarding the sensitivity to process parameter variability has been proven around the determined optimum. Thereby, a pharmaceutical production process has been investigated and significantly improved within seven 24 h cultivation cycles. Especially with regard to regulatory demands pointed out in the PAT initiative of the FDA, the combination of a highly instrumented, fully automated multi-bioreactor platform with proper cultivation strategies and extended DoE-software solutions opens up promising benefits and opportunities for pharmaceutical protein production stakeholders.

Plant biomass is the most abundant and usable carbon source for many fungal species. Due to its diverse and complex structure, fungi need to produce a large range of enzymes to degrade these polysaccharides into monomeric components. The fine-tuned production of such diverse enzyme sets requires control through a set of transcriptional regulators. Aspergillus has a strong potential for degrading biomass, thus this genus has become the most widely studied group of filamentous fungi in this area. This review examines Aspergillus as a successful degrader of plant polysaccharides, and reviews its potential in many industries such as biofuel and as a production host of homologous and heterologous proteins.

Aspergillus have become the most widely studied filamentous fungi due to its potential in biomass degradation. This review presents an outline of the complex structure of plant biomass, the large range of diverse enzymes produced by Aspergillus specifically tailored to its degradation and the regulatory systems involved in the expression of the corresponding genes.

Biocatalysis and Enzyme Technology – the second edition of this text book by Klaus Buchholz, Volker Kasche and Uwe Theo Bornscheuer is updated to provide the latest information in biocatalysis and enzyme technology. Book review by Mikhail Rabinovich.

Generally, recombinant and native microorganisms can be employed as whole-cell catalysts. The application of native hosts, however, shortens the process development time by avoiding multiple steps of strain construction. Herein, we studied the NAD(P)H-dependent reduction of o-chloroacetophenone by isolated xylose reductases and their native hosts Candida tenuis and Pichia stipitis. The natural hosts were benchmarked against Escherichia coli strains co-expressing xylose reductase and a dehydrogenase for co-enzyme recycling. Xylose-grown cells of C. tenuis and P. stipitis displayed specific o-chloroacetophenone reductase activities of 366 and 90 U g_CDW^–1, respectively, in the cell-free extracts. Fresh biomass was employed in batch reductions of 100 mM o-chloroacetophenone using glucose as co-substrate. Reaction stops at a product concentration of about 15 mM, which suggests sensitivity of the catalyst towards the formed product. In situ substrate supply and product removal by the addition of 40% hexane increased catalyst stability. Optimisation of the aqueous phase led to a (S)-1-(2-chlorophenyl)ethanol concentration of 71 mM (ee > 99.9%) obtained with 44 g_CDW L^–1 of C. tenuis. The final difference in productivities between native C. tenuis and recombinant E. coli was < 1.7-fold. The optically pure product is a required key intermediate in the synthesis of a new class of chemotherapeutic substances (polo-like kinase 1 inhibitors).

Enantiopure alcohols are frequently produced by the bioreduction of prochiral ketones. The application of native strains as whole cell catalysts provides a simple method towards chiral alcohols consisting of cell cultivation, whole cell reduction and product analysis. (S)-1-(2-chlorophenyl)ethanol, the product of the presented bioreduction, is a key intermediate in the synthesis of a new class of chemotherapeutic substances (polo-like kinase 1 inhibitors).

Probiotic bacteria harbor effector molecules that confer health benefits, but also adaptation factors that enable them to persist in the gastrointestinal tract of the consumer. To study these adaptation factors, an antibiotic-resistant derivative of the probiotic model organism Lactobacillus plantarum WCFS1 was repeatedly exposed to the mouse digestive tract by three consecutive rounds of (re)feeding of the longest persisting colonies. This exposure to the murine intestine allowed the isolation of intestine-adapted derivatives of the original strain that displayed prolonged digestive tract residence time. Re-sequencing of the genomes of these adapted derivatives revealed single nucleotide polymorphisms as well as a single nucleotide insertion in comparison with the genome of the original WCFS1 strain. Detailed in silico analysis of the identified genomic modifications pinpointed that alterations in the coding regions of genes encoding cell envelope associated functions and energy metabolism appeared to be beneficial for the gastrointestinal tract survival of L. plantarum WCFS1. This work demonstrates the feasibility of experimental evolution for the enhancement of the gastrointestinal residence time of probiotic strains, while full-genome re-sequencing of the adapted isolates provided clues towards the bacterial functions involved. Enhanced gastrointestinal residence is industrially relevant because it enhances the efficacy of the delivery of viable probiotics in situ.

Probiotic bacteria harbor adaptation factors that enable them to persist in the gastrointestinal tract of the consumer. The work presented here demonstrates the feasibility of experimental evolution for the enhancement of the gastrointestinal residence time of probiotic strains, while full-genome re-sequencing of the adapted isolates provides clues towards the bacterial functions involved in the altered phenotype.

Polymer monoliths are an efficient platform for antibody purification. The use of monoclonal antibodies (mAbs) and engineered antibody structures as therapeutics has increased exponentially over the past few decades. Several approaches use polymer monoliths to purify large quantities of antibody with defined clinical and performance requirements. Functional monolithic supports have attracted a great deal of attention as they offer practical advantages for antibody purification, such as more rapid analysis, smaller sample volume requirements and the opportunity for a greater target molecule enrichment. This review focuses on the development of synthetic and natural polymer-based monoliths for antibody purification. The materials and methods employed in monolith production are discussed, highlighting the properties of each system. We also review the structural characterization techniques available using monolithic systems and their performance under different chromatographic approaches to antibody capture and release. Finally, a summary of monolithic platforms developed for antibody separation is presented, as well as expected trends in research to solve current and future challenges in this field. This review comprises a comprehensive analysis of proposed solutions highlighting the remarkable potential of monolithic platforms.

Monoliths are an efficient platform for antibody purification. The use of monoclonal antibodies (MAbs) and engineered antibody structures as effective therapeutics for cancer, autoimmune, inflammation and infectious diseases has increased exponentially with an annual market of tens of billions of US dollars. Thus, several approaches involving monoliths for the production and purification of large antibody quantities with defined clinical and performance requirements have been developed. This review comprises a comprehensive analysis of proposed solutions highlighting the remarkable achievements of this platform for the antibody purification field even at a commercial level.

Industrial biotechnology is playing an important role in the transition to a bio-based economy. Currently, however, industrial implementation is still modest, despite the advances made in microorganism development. Given that the fuels and commodity chemicals sectors are characterized by tight economic margins, we propose to address overall process design and efficiency at the start of bioprocess development. While current microorganism development is targeted at product formation and product yield, addressing process design at the start of bioprocess development means that microorganism selection can also be extended to other critical targets for process technology and process scale implementation, such as enhancing cell separation or increasing cell robustness at operating conditions that favor the overall process. In this paper we follow this approach for the microbial production of diesel-like biofuels. We review current microbial routes with both oleaginous and engineered microorganisms. For the routes leading to extracellular production, we identify the process conditions for large scale operation. The process conditions identified are finally translated to microorganism development targets. We show that microorganism development should be directed at anaerobic production, increasing robustness at extreme process conditions and tailoring cell surface properties. All the same time, novel process configurations integrating fermentation and product recovery, cell reuse and low-cost technologies for product separation are mandatory. This review provides a state-of-the-art summary of the latest challenges in large-scale production of diesel-like biofuels.

In order to increase industrial implementation, process design should be imposed at the start of bioprocess development. This allows for timely generation of large scale-based targets for microorganism development and novel process configurations. In this review, the authors analyze the microbial production of diesel-like molecules, showing that microorganism development should be directed towards anaerobic production, increasing robustness at extreme process conditions and tailoring cell surface properties, while novel process configurations integrating fermentation and product recovery, cell reuse and low-cost technologies for product separation are mandatory.

Biotechnology Journal announces our second biotechnology essay competition with the theme “biotechnology and sustainable food practices”, open to all undergraduate students.

The many clinical trials currently in progress will likely lead to the widespread use of stem cell-based therapies for an extensive variety of diseases, either in autologous or allogeneic settings. With the current pace of progress, in a few years' time, the field of stem cell-based therapy should be able to respond to the market demand for safe, robust and clinically efficient stem cell-based therapeutics. Due to the limited number of stem cells that can be obtained from a single donor, one of the major challenges on the roadmap for regulatory approval of such medicinal products is the expansion of stem cells using Good Manufacturing Practices (GMP)-compliant culture systems. In fact, manufacturing costs, which include production and quality control procedures, may be the main hurdle for developing cost-effective stem cell therapies. Bioreactors provide a viable alternative to the traditional static culture systems in that bioreactors provide the required scalability, incorporate monitoring and control tools, and possess the operational flexibility to be adapted to the differing requirements imposed by various clinical applications. Bioreactor systems face a number of issues when incorporated into stem cell expansion protocols, both during development at the research level and when bioreactors are used in on-going clinical trials. This review provides an overview of the issues that must be confronted during the development of GMP-compliant bioreactors systems used to support the various clinical applications employing stem cells.

With the current pace of progress, in a few years, stem cell-based therapy should be able to respond to the market demand for safe, robust and clinically efficient stem cell-based therapeutics. Bioreactors provide a viable alternative to the traditional static culture systems in that they provide the required scalability, monitoring and control tools, as well as the operational flexibility to be adapted to the different requirements of various clinical applications.

The 8^th HIC/RPC Bioseparation Conference was held from March 3–7, 2013 at the Westin Savannah Harbor Resort in Savannah, Georgia, USA. Read this meeting report to find out more on the latest developments in chromatography for bioprocess applications.

Altered metabolism is linked to the appearance of various human diseases and a better understanding of disease-associated metabolic changes may lead to the identification of novel prognostic biomarkers and the development of new therapies. Genome-scale metabolic models (GEMs) have been employed for studying human metabolism in a systematic manner, as well as for understanding complex human diseases. In the past decade, such metabolic models – one of the fundamental aspects of systems biology – have started contributing to the understanding of the mechanistic relationship between genotype and phenotype. In this review, we focus on the construction of the Human Metabolic Reaction database, the generation of healthy cell type- and cancer-specific GEMs using different procedures, and the potential applications of these developments in the study of human metabolism and in the identification of metabolic changes associated with various disorders. We further examine how in silico genome-scale reconstructions can be employed to simulate metabolic flux distributions and how high-throughput omics data can be analyzed in a context-dependent fashion. Insights yielded from this mechanistic modeling approach can be used for identifying new therapeutic agents and drug targets as well as for the discovery of novel biomarkers. Finally, recent advancements in genome-scale modeling and the future challenge of developing a model of whole-body metabolism are presented. The emergent contribution of GEMs to personalized and translational medicine is also discussed.

Genome-scale metabolic models in the Human Metabolic Atlas facilitates the understanding of molecular mechanisms behind the etiology of metabolic diseases, with the goal of identifying novel biomarkers and therapeutic targets. These models can be used for simulation of whole-body metabolic functions using flux balance analysis and contribute to the development of personalized and translational medicine.

Selection markers are common genetic elements used in recombinant cell line development. While several selection systems exist for use in mammalian cell lines, no previous study has comprehensively evaluated their performance in the isolation of recombinant populations and cell lines. Here we examine four antibiotics, hygromycin B, neomycin, puromycin, and Zeocin™, and their corresponding selector genes, using a green fluorescent protein (GFP) as a reporter in two model cell lines, HT1080 and HEK293. We identify Zeocin™ as the best selection agent for cell line development in human cells. In comparison to the other selection systems, Zeocin™ is able to identify populations with higher fluorescence levels, which in turn leads to the isolation of better clonal populations and less false positives. Furthermore, Zeocin™-resistant populations exhibit better transgene stability in the absence of selection pressure compared to other selection agents. All isolated Zeocin™-resistant clones, regardless of cell type, exhibited GFP expression. By comparison, only 79% of hygromycin B-resistant, 47% of neomycin-resistant, and 14% of puromycin-resistant clones expressed GFP. Based on these results, we rank Zeocin™ > hygromycin B ∼ puromycin > neomycin for cell line development in human cells. Furthermore, this study demonstrates that selection marker choice does indeed impact cell line development.

Selection markers are important genetic elements that enable the development of recombinant mammalian cell lines. In this study, the authors evaluate the performance of four common selection markers and their corresponding selection agents in two human cell lines. They also examine the quality and stability of the recombinant populations and the resulting single cell populations using a GFP reporter construct. This study demonstrates that selection marker choice impacts cell line development.

Monoclonal antibodies (mAbs) are important therapeutic proteins. One of the challenges facing large-scale production of monoclonal antibodies is the capacity bottleneck in downstream processing, which can be circumvented by using magnetic stimuli-responsive polymer nanoparticles. In this work, stimuli-responsive magnetic particles composed of a magnetic poly(methyl methacrylate) core with a poly(N-isopropylacrylamide-co-acrylic acid) (P(NIPAM-co-AA)) shell cross-linked with N, N'-methylenebisacrylamide were prepared by miniemulsion polymerization. The particles were shown to have an average hydrodynamic diameter of 317 nm at 18°C, which decreased to 277 nm at 41°C due to the collapse of the thermo-responsive shell. The particles were superparamagnetic in behavior and exhibited a saturation magnetization of 12.6 emu/g. Subsequently, we evaluated the potential of these negatively charged stimuli-responsive magnetic particles in the purification of a monoclonal antibody from a diafiltered CHO cell culture supernatant by cation exchange. The adsorption of antibodies onto P(NIPAM-co-AA)-coated nanoparticles was highly selective and allowed for the recovery of approximately 94% of the mAb. Different elution strategies were employed providing highly pure mAb fractions with host cell protein (HCP) removal greater than 98%. By exploring the stimuli-responsive properties of the particles, shorter magnetic separation times were possible without significant differences in product yield and purity.

Monoclonal antibodies are very important therapeutic proteins with wide applications in medicine. One of the challenges facing large-scale production of monoclonal antibodies is separation efficiency during the purification process. High binding capacities of small sized magnetic adsorbents can potentially circumvent foreseeable capacity limitations in the downstream processing of monoclonal antibodies. With stimuli-responsive magnetic particles the authors have obtained high binding capacities and fast separation times that can be improved by stimuli-induced particle aggregation.

Biocompatibility of polymers is an important parameter for the successful application of polymers in tissue engineering. In this work, quartz crystal microbalance (QCM) devices were used to follow the adhesion of NIH 3T3 fibroblasts to QCM surfaces modified with fibronectin (FN) and poly-D-lysine (PDL). The variations in sensor resonant frequency (Δf) and motional resistance (ΔR), monitored as the sensor signal, revealed that cell adhesion was favored in the PDL-coated QCMs. Fluorescence microscopy images of seeded cells showed more highly spread cells on the PDL substrate, which is consistent with the results of the QCM signals. The sensor signal was shown to be sensitive to extracellular matrix (ECM)-binding motifs. Ethylenediaminetetraacetic acid (EDTA) and soluble Gly-Arg-Gly-Asp-Ser (GRGDS) peptides were used to interfere with cell-ECM binding motifs onto FN-coated QCMs. The acquired acoustic signals successfully showed that in the presence of 30 mM EDTA or 1 mM GRGDS, cell adhesion is almost completely abolished due to the inhibition/blocking of integrin function by these compounds. The results presented here demonstrate the potential of the QCM sensor to study cell adhesion, to monitor the biocompatibility of polymers and materials, and to assess the effect of adhesion modulators. QCM sensors have great potential in tissue engineering applications, as QCM sensors are able to analyze the biocompatibility of surfaces and it has the added advantage of being able to evaluate, in situ and in real time, the effect of specific drugs/treatments on cells.

The propagation of acoustic waves from a quartz crystal microbalance (QCM) sensor can be used to monitor the growth of adherent cells. This study demonstrates that the signal generated by QCM sensors changes according to cell morphology, a unique property that can be explored for surface material biocompatibility studies. QCM sensors therefore have great potential in tissue engineering applications.

Methanosarcina barkeri is an Archaeon that produces methane anaerobically as the primary byproduct of its metabolism. M. barkeri can utilize several substrates for ATP and biomass production including methanol, acetate, methyl amines, and a combination of hydrogen and carbon dioxide. In 2006, a metabolic reconstruction of M. barkeri, iAF692, was generated based on a draft genome annotation. The iAF692 reconstruction enabled the first genome-Scale simulations for Archaea. Since the publication of the first metabolic reconstruction of M. barkeri, additional genomic, biochemical, and phenotypic data have clarified several metabolic pathways. We have used this newly available data to improve the M. barkeri metabolic reconstruction. Modeling simulations using the updated model, iMG746, have led to increased accuracy in predicting gene knockout phenotypes and simulations of batch growth behavior. We used the model to examine knockout lethality data and make predictions about metabolic regulation under different growth conditions. Thus, the updated metabolic reconstruction of M. barkeri metabolism is a useful tool for predicting cellular behavior, studying the methanogenic lifestyle, guiding experimental studies, and making predictions relevant to metabolic engineering applications.

Improving the accuracy and scope of a Methanosarcina barkeri metabolic model: M. barkeri is a single-celled organism that naturally produces methane, the main ingredient of natural gas, as a byproduct of its metabolism. A metabolic model of methanogenesis exists, but many experiments on methanogenesis have since been done which have greatly furthered our understanding of this critical process. In this article, the authors update and validate the model to reflect this new data and use the new model to predict regulatory mechanisms for certain key metabolic enzymes.

With recent advances in metabolic engineering, it is now technically possible to produce a wide portfolio of existing petrochemical products from biomass feedstock. In recent years, a number of modeling approaches have been developed to support the engineering and decision-making processes associated with the development and implementation of a sustainable biochemical industry. The temporal and spatial scales of modeling approaches for sustainable chemical production vary greatly, ranging from metabolic models that aid the design of fermentative microbial strains to material and monetary flow models that explore the ecological impacts of all economic activities. Research efforts that attempt to connect the models at different scales have been limited. Here, we review a number of existing modeling approaches and their applications at the scales of metabolism, bioreactor, overall process, chemical industry, economy, and ecosystem. In addition, we propose a multi-scale approach for integrating the existing models into a cohesive framework. The major benefit of this proposed framework is that the design and decision-making at each scale can be informed, guided, and constrained by simulations and predictions at every other scale. In addition, the development of this multi-scale framework would promote cohesive collaborations across multiple traditionally disconnected modeling disciplines to achieve sustainable chemical production.

Multi-scale modeling of sustainable chemical production – the authors review a number of existing modeling approaches and their applications at the scales of metabolism, bioreactor, overall process, chemical industry, economy, and ecosystem. In addition, the authors propose a multi-scale approach for integrating the existing models into a cohesive framework.

Haloalkane dehalogenases are microbial enzymes with a wide range of biotechnological applications, including biocatalysis. The use of organic co-solvents to solubilize their hydrophobic substrates is often necessary. In order to choose the most compatible co-solvent, the effects of 14 co-solvents on activity, stability and enantioselectivity of three model enzymes, DbjA, DhaA, and LinB, were evaluated. All co-solvents caused at high concentration loss of activity and conformational changes. The highest inactivation was induced by tetrahydrofuran, while more hydrophilic co-solvents, such as ethylene glycol and dimethyl sulfoxide, were better tolerated. The effects of co-solvents at low concentration were different for each enzyme-solvent pair. An increase in DbjA activity was induced by the majority of organic co-solvents tested, while activities of DhaA and LinB decreased at comparable concentrations of the same co-solvent. Moreover, a high increase of DbjA enantioselectivity was observed. Ethylene glycol and 1,4-dioxane were shown to have the most positive impact on the enantioselectivity. The favorable influence of these co-solvents on both activity and enantioselectivity makes DbjA suitable for biocatalytic applications. This study represents the first investigation of the effects of organic co-solvents on the biocatalytic performance of haloalkane dehalogenases and will pave the way for their broader use in industrial processes.

Organic co-solvents can improve activity and enantioselectivity of haloalkane dehalogenases: in an effort to choose the most compatible organic co-solvent for haloalkane dehalogenases, the effects of fourteen co-solvents on the activity, stability and enantioselectivity of three model enzymes were systematically evaluated. The study demonstrates the favorable effect of the co-solvents at low concentrations on both activity and enantioselectivity of one of the tested haloalkane dehalogenases, DbjA, making this enzyme a potential catalyst for commercial biocatalytic applications.

Recent progress in understanding the molecular basis of autophagy has demonstrated its importance in several areas of human health. Affordable screening techniques with higher sensitivity and specificity to identify autophagy are, however, needed to move the field forward. In fact, only laborious and/or expensive methodologies such as electron microscopy, dye-staining of autophagic vesicles, and LC3-II immunoblotting or immunoassaying are available for autophagy identification. Aiming to fulfill this technical gap, we describe here the association of three widely used assays to determine cell viability – Crystal Violet staining (CVS), 3-[4, 5-dimethylthiaolyl]-2, 5-diphenyl-tetrazolium bromide (MTT) reduction, and neutral red uptake (NRU) – to predict autophagic cell death in vitro. The conceptual framework of the method is the superior uptake of NR in cells engaging in autophagy. NRU was then weighted by the average of MTT reduction and CVS allowing the calculation of autophagic arbitrary units (AAU), a numeric variable that correlated specifically with the autophagic cell death. The proposed strategy is very useful for drug discovery, allowing the investigation of potential autophagic inductor agents through a rapid screening using mammalian cell lines B16-F10, HaCaT, HeLa, MES-SA, and MES-SA/Dx5 in a unique single microplate.

Although recent progress in understanding the molecular basis of autophagy has demonstrated its importance for human health, affordable screening techniques to identify autophagy in vitro are not yet available. In this article, the authors describe a novel in vitro strategy for rapid screening of potential autophagic inductor agents using mammalian cell lines. The proposed strategy is very useful for drug discovery, allowing the investigation of potential autophagic inductors in a single microplate. The conceptual framework of the method is the superior uptake of Neutral Red (NR) in cells engaging in autophagy.

As large volumes of omics data have become available, systems biology is playing increasingly important roles in elucidating new biological phenomena, especially through genome-scale metabolic network modeling and simulation. Much effort has been exerted on integrating omics data with metabolic flux simulation, but further development is necessary for more accurate flux estimation. To move one step forward, we adopted the concept of flux-coupled genes (FCGs), which show that their expression transition patterns upon perturbations are correlated with their corresponding flux values, as additional constraints in metabolic flux analysis. It was found that gnd, pfkB, rpe, sdhB, sdhD, sucA, and zwf genes, mostly associated with pentose phosphate pathway and TCA cycle, were the most consistent FCGs in Escherichia coli based on its transcriptome and ¹³C-flux data obtained from the chemostat cultivation at five different dilution rates. Consequently, constraints-based flux analyses with FCGs as additional constraints were conducted for the seven single-gene knockout mutants, compared with those obtained without using FCGs. This strategy of constraining the metabolic flux analysis with FCGs is expected to be useful due to the relative ease in obtaining transcriptional information in the functional genomics era.

Expression levels of flux-coupled genes (FCGs) provide additional information for the constraints-based flux analysis: hypothesizing that there exist genes whose expression levels correlate with their respective flux values across different conditions, the authors identified seven FCGs from the transcriptome and ¹³C-flux data of Escherichia coli. Constraints-based flux analysis with the FCGs captured greater number of correct flux changes than conventionally used methods, FBA and MOMA. Because transcriptional information on genes is routinely obtainable, this approach using FCGs will be useful in accurately determining flux values under varying conditions.

The analysis of host cell proteins (HCPs) is one of the most important analytical requirements during bioprocess development of therapeutic moieties. In this review, we focus on the comparison of different methods for the analysis of HCPs and how cell lines, fermentation conditions, and unit operations influence HCP distribution during the process chain. Current guidelines typically require reduction of HCPs to the ppm level, depending on the intended use, the route of administration of the product, and the production system. A range of immunospecific and non-specific methods are available that have been globally accepted by regulatory bodies. Immunospecific methods, such as ELISA, are simple to use in routine analysis and can quantify low levels of HCPs when specific antibodies are available. Non-specific methods are more complex; however, they provide a holistic view of the HCP profile and qualitative information of the composition of HCP in the sample. Different methods for the comparison of bioprocessing strategies during scale-up and purification development are compared herein. The methods include immunospecific methods, such as ELISA, western blot, and threshold, and non-specific methods, such as 2D-DIGE and 2D-HPLC combined with MS.

Host cell protein content is one of the three most important quality criteria of biopharmaceuticals. In this review, the authors compare different methods for analysis of host cell proteins, and how cell lines, fermentation conditions, and unit operations influence host cell protein distribution during the bioprocess chain.

Systems metabolic engineering is becoming a widely-evoked paradigm for industrial strain design and optimization. Specifically, systems wide experimental and computational analyses of cells and their environments enable guide metabolic engineers to quickly parse the genome and creating desirable overproduction phenotypes.

Systems Metabolic Enginering, edited by Christoph Wittmann and Sang Yup Lee “sits at the crossroads of being an introductory book providing an overview of the field and a handy desk-reference for state-of-the-art case studies for the expert metabolic engineer”. Read this book review by Hal Alper.

Recombineering has been an essential tool for genetic engineering in microbes for many years and has enabled faster, more efficient engineering than previous techniques. There have been numerous studies that focus on improving recombineering efficiency, which can be divided into three main areas: (i) optimizing the oligo used for recombineering to enhance replication fork annealing and limit proofreading; (ii) mechanisms to modify the replisome itself, enabling an increased rate of annealing; and (iii) multiplexing recombineering targets and automation. These efforts have increased the efficiency of recombineering several hundred-fold. One area that has received far less attention is the problem of multiple chromosomes, which effectively decrease efficiency on a chromosomal basis, resulting in more sectored colonies, which require longer outgrowth to obtain clonal populations. Herein, we describe the problem of multiple chromosomes, discuss calculations predicting how many generations are needed to obtain a pure colony, and how changes in experimental procedure or genetic background can minimize the effect of multiple chromosomes.

Recombineering has enabled fast and efficient editing and rewriting of the chromosome in E. coli; however, the presence of multiple chromosomes requires longer out growth to obtain clonal colonies. The longer outgrowth period can be minimized by changes to the replisome or by performing multiple recombineering events with the same pool of oligos.

Accelerating the process of industrial bacterial host strain development, aimed at increasing productivity, generating new bio-products or utilizing alternative feedstocks, requires the integration of complementary approaches to manipulate cellular metabolism and regulatory networks. Systems metabolic engineering extends the concept of classical metabolic engineering to the systems level by incorporating the techniques used in systems biology and synthetic biology, and offers a framework for the development of the next generation of industrial strains. As one of the most useful tools of systems metabolic engineering, protein design allows us to design and optimize cellular metabolism at a molecular level. Here, we review the current strategies of protein design for engineering cellular synthetic pathways, metabolic control systems and signaling pathways, and highlight the challenges of this subfield within the context of systems metabolic engineering.

Within the context of systems metabolic engineering, protein design can be utilized to introduce new enzyme function or substrate spectrum for the construction of novel synthetic pathways. This review discusses tools such as improving enzyme properties for the optimization of pathway efficiency, altering the specificity of transcription regulators or allosteric proteins to rewire regulation network, or building protein scaffolds for metabolite channeling or altering signaling transduction pathways – all of which could be combined to accelerate the process of industrial strain development.

Recently genome sequence data have become available for Aspergillus and Pichia species of industrial interest. This has stimulated the use of systems biology approaches for large-scale analysis of the molecular and metabolic responses of Aspergillus and Pichia under defined conditions, which has resulted in much new biological information. Case-specific contextualization of this information has been performed using comparative and functional genomic tools. Genomics data are also the basis for constructing genome-scale metabolic models, and these models have helped in the contextualization of knowledge on the fundamental biology of Aspergillus and Pichia species. Furthermore, with the availability of these models, the engineering of Aspergillus and Pichia is moving from traditional approaches, such as random mutagenesis, to a systems metabolic engineering approach. Here we review the recent trends in systems biology of Aspergillus and Pichia species, highlighting the relevance of these developments for systems metabolic engineering of these organisms for the production of hydrolytic enzymes, biofuels and chemicals from biomass.

Metabolic engineering is moving from traditional methods such as random mutagenesis to a systems level which decreases the time and efforts on design and implementation. Here, the authors review the recent trends in systems biology of Aspergillus and Pichia species, highlighting the relevance of developments for systems metabolic engineering of these organisms for the production of hydrolytic enzymes, biofuels and chemicals from biomass.

Protein engineering in the context of metabolic engineering is increasingly important to the field of industrial biotechnology. As the demand for biologically produced food, fuels, chemicals, food additives, and pharmaceuticals continues to grow, the ability to design and modify proteins to accomplish new functions will be required to meet the high productivity demands for the metabolism of engineered organisms. We review advances in selecting, modeling, and engineering proteins to improve or alter their activity. Some of the methods have only recently been developed for general use and are just beginning to find greater application in the metabolic engineering community. We also discuss methods of generating random and targeted diversity in proteins to generate mutant libraries for analysis. Recent uses of these techniques to alter cofactor use; produce non-natural amino acids, alcohols, and carboxylic acids; and alter organism phenotypes are presented and discussed as examples of the successful engineering of proteins for metabolic engineering purposes.

Engineering of proteins for control of metabolism is important to the field of industrial biotechnology. The authors review current and next-generation tools enabling scientists to modify proteins in a rationally designed manner and present several examples of the use of engineered proteins to produce desired compounds via altering the metabolism of living organisms. The use of these tools has shown the promise of protein engineering, and these tools continued development over the next few years will lead to an increase in the numbers and types of chemicals that can be produced by engineered organisms.

The sustainable production of industrial platform chemicals is one of the great challenges facing the biotechnology field. Ideally, fermentation feedstocks would rather rely on industrial waste streams than on food-based raw materials. Corynebacterium glutamicum was metabolically engineered to produce the bio-nylon precursor 1,5-diaminopentane from the hemicellulose sugar xylose. Comparison of a basic diaminopentane producer strain on xylose and glucose feedstocks revealed a 30% reduction in diaminopentane yield and productivity on the pentose sugar. The integration of in vivo and in silico metabolic flux analysis by ¹³C and elementary modes identified bottlenecks in the pentose phosphate pathway and the tricarboxylic acid cycle that limited performance on xylose. By the integration of global transcriptome profiling, this could be specifically targeted to the tkt operon, genes that encode for fructose bisphosphatase (fbp) and isocitrate dehydrogenase (icd), and to genes involved in formation of lysine (lysE) and N-acetyl diaminopentane (act). This was used to create the C. glutamicum strain DAP-Xyl1 icd^GTG P_eftufbp P_sodtkt Δact ΔlysE. The novel producer, designated DAP-Xyl2, exhibited a 54% increase in product yield to 233 mmol mol^–1 and a 100% increase in productivity to 1 mmol g^–1 h^–1 on the xylose substrate. In a fed-batch process, the strain achieved 103 g L^–1 of diaminopentane from xylose with a product yield of 32%. Xylose utilization is currently one of the most relevant metabolic engineering subjects. In this regard, the current work is a milestone in industrial strain engineering of C. glutamicum.

See accompanying commentary by Hiroshi Shimizu DOI: 10.1002/biot.201300097

The sustainable production of chemicals from renewable, non-food raw materials is one of the great challenges in industrial biotechnology. In this article, the authors report reprogramming of Corynebacterium glutamicum for efficient 1,5-diaminopentane production from xylose via systems metabolic engineering. Reaching a yield of 32% and a maximum titer of 103 gL^–1, the current work reaches a milestone in industrial strain engineering with C. glutamicum.

Cyanobacteria have received considerable attention as a sustainable energy resource because of their organic material production capacity using light energy and CO₂ as a carbon source. Therefore, it is important to understand the cellular metabolism of cyanobacteria for metabolic engineering. In this study, to shed light on the central metabolism of cyanobacteria, we performed transcriptomic and metabolomic analyses of a glucose-tolerant strain of the cyanobacterium Synechocystis sp. PCC 6803, which was cultured under auto- and mixotrophic conditions. Our results indicate that the oxidative pentose phosphate pathway and glycolysis are activated under mixotrophic conditions rather than autotrophic conditions. Moreover, we examined the effect of atrazine, a photosynthesis inhibitor, on the metabolism of PCC 6803 under mixotrophic conditions, which was defined as photoheterotrophic conditions, by transcriptomics and metabolomics. We demonstrated that the activity of the glycolytic pathway decreased due to the indirect effect of atrazine. In addition, the difference in transcriptional and metabolic changes between auto- and photoheterotrophic conditions could also be captured. The omics dataset reported herein provides clues for understanding the metabolism of cyanobacteria.

It is important to understand the cellular metabolism of cyanobacteria for metabolic engineering. In this study, authors perform transcriptomic and metabolomic analyses of the central metabolism of Synechocystis sp. PCC 6803 which was cultured under different trophic conditions. They show that the oxidative pentose phosphate pathway and glycolysis are activated under mixotrophic condition rather than autotrophic condition. By examining the effect of atrazine, they also show that the activity of the glycolytic pathway is decreased due to the indirect effect of atrazine. The omics dataset reported herein provides clues for understanding the metabolism of cyanobacteria.

Optimized production of bio-based fuels and chemicals from microbial cell factories is a central goal of systems metabolic engineering. To achieve this goal, a new computational method of using flux balance analysis with flux ratios (FBrAtio) was further developed in this research and applied to five case studies to evaluate and design metabolic engineering strategies. The approach was implemented using publicly available genome-scale metabolic flux models. Synthetic pathways were added to these models along with flux ratio constraints by FBrAtio to achieve increased (i) cellulose production from Arabidopsis thaliana; (ii) isobutanol production from Saccharomyces cerevisiae; (iii) acetone production from Synechocystis sp. PCC6803; (iv) H₂ production from Escherichia coli MG1655; and (v) isopropanol, butanol, and ethanol (IBE) production from engineered Clostridium acetobutylicum. The FBrAtio approach was applied to each case to simulate a metabolic engineering strategy already implemented experimentally, and flux ratios were continually adjusted to find (i) the end-limit of increased production using the existing strategy, (ii) new potential strategies to increase production, and (iii) the impact of these metabolic engineering strategies on product yield and culture growth. The FBrAtio approach has the potential to design “fine-tuned” metabolic engineering strategies in silico that can be implemented directly with available genomic tools.

Constraining the distribution of a single metabolite among competing pathways can massively reorder global metabolism. In this study, authors show a new method – Flux Balance Analysis with Flux Ratios (FBrAtio) – along with genome-scale modeling, which can be used to determine the global outcome of metabolic engineering strategies. The authors apply this technique to five case studies to evaluate and improve existing metabolic engineering strategies in the production of biofuels and commodity chemicals.

In recent years, a growing number of metabolic engineering strain design techniques have employed constraint-based modeling to determine metabolic and regulatory network changes which are needed to improve chemical production. These methods use systems-level analysis of metabolism to help guide experimental efforts by identifying deletions, additions, downregulations, and upregulations of metabolic genes that will increase biological production of a desired metabolic product. In this work, we propose a new strain design method with continuous modifications (CosMos) that provides strategies for deletions, downregulations, and upregulations of fluxes that will lead to the production of the desired products. The method is conceptually simple and easy to implement, and can provide additional strategies over current approaches. We found that the method was able to find strain design strategies that required fewer modifications and had larger predicted yields than strategies from previous methods in example and genome-scale networks. Using CosMos, we identified modification strategies for producing a variety of metabolic products, compared strategies derived from Escherichia coli and Saccharomyces cerevisiae metabolic models, and examined how imperfect implementation may affect experimental outcomes. This study gives a powerful and flexible technique for strain engineering and examines some of the unexpected outcomes that may arise when strategies are implemented experimentally.

Strain design algorithms are used to facilitate metabolic engineering efforts by systematically identifying genetic changes, which are needed to make to an organism increase production of a desired chemical. This study presents a new strain design algorithm, which uses continuous modifications (CosMos) and identifies how much fluxes need to change via up/down regulation to guarantee chemical production.

Identifying multiple enzyme targets for metabolic engineering is very critical for redirecting cellular metabolism to achieve desirable phenotypes, e.g., overproduction of a target chemical. The challenge is to determine which enzymes and how much of these enzymes should be manipulated by adding, deleting, under-, and/or over-expressing associated genes. In this study, we report the development of a systematic multiple enzyme targeting method (SMET), to rationally design optimal strains for target chemical overproduction. The SMET method combines both elementary mode analysis and ensemble metabolic modeling to derive SMET metrics including l-values and c-values that can identify rate-limiting reaction steps and suggest which enzymes and how much of these enzymes to manipulate to enhance product yields, titers, and productivities. We illustrated, tested, and validated the SMET method by analyzing two networks, a simple network for concept demonstration and an Escherichia coli metabolic network for aromatic amino acid overproduction. The SMET method could systematically predict simultaneous multiple enzyme targets and their optimized expression levels, consistent with experimental data from the literature, without performing an iterative sequence of single-enzyme perturbation. The SMET method was much more efficient and effective than single-enzyme perturbation in terms of computation time and finding improved solutions.

The Systematic Multiple Enzyme Targeting (SMET) is a novel method to rationally design optimal strains for target chemical overproduction. SMET combines both elementary mode analysis and ensemble metabolic modeling to derive SMET metrics including l- and c-values that can identify rate-limiting reaction steps and suggest which enzymes and how much of these enzymes to manipulate to enhance product yields, titers, and productivities. This method could systematically predict simultaneous multiple enzyme targets and their optimized expression levels, consistent with experimental data from the literature, without performing an iterative sequence of single-enzyme perturbation.

Cyanobacteria are ideal metabolic engineering platforms for carbon-neutral biotechnology because they directly convert CO₂ to a range of valuable products. In this study, we present a computational assessment of biochemical production in Synechococcus sp. PCC 7002 (Synechococcus 7002), a fast growing cyanobacterium whose genome has been sequenced, and for which genetic modification methods have been developed. We evaluated the maximum theoretical yields (mol product per mol CO₂ or mol photon) of producing various chemicals under photoautotrophic and dark conditions using a genome-scale metabolic model of Synechococcus 7002. We found that the yields were lower under dark conditions, compared to photoautotrophic conditions, due to the limited amount of energy and reductant generated from glycogen. We also examined the effects of photon and CO₂ limitations on chemical production under photoautotrophic conditions. In addition, using various computational methods such as minimization of metabolic adjustment (MOMA), relative metabolic change (RELATCH), and OptORF, we identified gene-knockout mutants that are predicted to improve chemical production under photoautotrophic and/or dark anoxic conditions. These computational results are useful for metabolic engineering of cyanobacteria to synthesize value-added products.

Cyanobacteria are ideal metabolic engineering platforms for carbon-neutral biotechnology. In this study, authors present a thorough computational evaluation of biofuel production in the fast growing cyanobacterium Synechococcus 7002 in terms of yields, energy requirements, and predicted chemical production profiles for knockout mutants under different conditions. Computational strain design methods were able to predict mutants with improved chemical production for most products considered. These predictions will serve as a starting point for future metabolic engineering efforts to construct strains with improved biofuel production.