iAK692: a genome-scale metabolic model of Spirulina platensis C1.

PubMed

Klanchui, Amornpan; Khannapho, Chiraphan; Phodee, Atchara; Cheevadhanarak, Supapon; Meechai, Asawin

2012-06-15

Spirulina (Arthrospira) platensis is a well-known filamentous cyanobacterium used in the production of many industrial products, including high value compounds, healthy food supplements, animal feeds, pharmaceuticals and cosmetics, for example. It has been increasingly studied around the world for scientific purposes, especially for its genome, biology, physiology, and also for the analysis of its small-scale metabolic network. However, the overall description of the metabolic and biotechnological capabilities of S. platensis requires the development of a whole cellular metabolism model. Recently, the S. platensis C1 (Arthrospira sp. PCC9438) genome sequence has become available, allowing systems-level studies of this commercial cyanobacterium. In this work, we present the genome-scale metabolic network analysis of S. platensis C1, iAK692, its topological properties, and its metabolic capabilities and functions. The network was reconstructed from the S. platensis C1 annotated genomic sequence using Pathway Tools software to generate a preliminary network. Then, manual curation was performed based on a collective knowledge base and a combination of genomic, biochemical, and physiological information. The genome-scale metabolic model consists of 692 genes, 837 metabolites, and 875 reactions. We validated iAK692 by conducting fermentation experiments and simulating the model under autotrophic, heterotrophic, and mixotrophic growth conditions using COBRA toolbox. The model predictions under these growth conditions were consistent with the experimental results. The iAK692 model was further used to predict the unique active reactions and essential genes for each growth condition. Additionally, the metabolic states of iAK692 during autotrophic and mixotrophic growths were described by phenotypic phase plane (PhPP) analysis. This study proposes the first genome-scale model of S. platensis C1, iAK692, which is a predictive metabolic platform for a global understanding of

Dynamic genome-scale metabolic modeling of the yeast Pichia pastoris.

PubMed

Saitua, Francisco; Torres, Paulina; Pérez-Correa, José Ricardo; Agosin, Eduardo

2017-02-21

Pichia pastoris shows physiological advantages in producing recombinant proteins, compared to other commonly used cell factories. This yeast is mostly grown in dynamic cultivation systems, where the cell's environment is continuously changing and many variables influence process productivity. In this context, a model capable of explaining and predicting cell behavior for the rational design of bioprocesses is highly desirable. Currently, there are five genome-scale metabolic reconstructions of P. pastoris which have been used to predict extracellular cell behavior in stationary conditions. In this work, we assembled a dynamic genome-scale metabolic model for glucose-limited, aerobic cultivations of Pichia pastoris. Starting from an initial model structure for batch and fed-batch cultures, we performed pre/post regression diagnostics to ensure that model parameters were identifiable, significant and sensitive. Once identified, the non-relevant ones were iteratively fixed until a priori robust modeling structures were found for each type of cultivation. Next, the robustness of these reduced structures was confirmed by calibrating the model with new datasets, where no sensitivity, identifiability or significance problems appeared in their parameters. Afterwards, the model was validated for the prediction of batch and fed-batch dynamics in the studied conditions. Lastly, the model was employed as a case study to analyze the metabolic flux distribution of a fed-batch culture and to unravel genetic and process engineering strategies to improve the production of recombinant Human Serum Albumin (HSA). Simulation of single knock-outs indicated that deviation of carbon towards cysteine and tryptophan formation improves HSA production. The deletion of methylene tetrahydrofolate dehydrogenase could increase the HSA volumetric productivity by 630%. Moreover, given specific bioprocess limitations and strain characteristics, the model suggests that implementation of a decreasing

Cyanobacterial metabolic engineering for biofuel and chemical production.

PubMed

Oliver, Neal J; Rabinovitch-Deere, Christine A; Carroll, Austin L; Nozzi, Nicole E; Case, Anna E; Atsumi, Shota

2016-12-01

Rising levels of atmospheric CO 2 are contributing to the global greenhouse effect. Large scale use of atmospheric CO 2 may be a sustainable and renewable means of chemical and liquid fuel production to mitigate global climate change. Photosynthetic organisms are an ideal platform for efficient, natural CO 2 conversion to a broad range of chemicals. Cyanobacteria are especially attractive for these purposes, due to their genetic malleability and relatively fast growth rate. Recent years have yielded a range of work in the metabolic engineering of cyanobacteria and have led to greater knowledge of the host metabolism. Understanding of endogenous and heterologous carbon regulation mechanisms leads to the expansion of productive capacity and chemical variety. This review discusses the recent progress in metabolic engineering of cyanobacteria for biofuel and bulk chemical production since 2014. Copyright © 2016 Elsevier Ltd. All rights reserved.

Genome-scale metabolic network of Cordyceps militaris useful for comparative analysis of entomopathogenic fungi.

PubMed

Vongsangnak, Wanwipa; Raethong, Nachon; Mujchariyakul, Warasinee; Nguyen, Nam Ninh; Leong, Hon Wai; Laoteng, Kobkul

2017-08-30

The first genome-scale metabolic network of Cordyceps militaris (iWV1170) was constructed representing its whole metabolisms, which consisted of 894 metabolites and 1,267 metabolic reactions across five compartments, including the plasma membrane, cytoplasm, mitochondria, peroxisome and extracellular space. The iWV1170 could be exploited to explain its phenotypes of growth ability, cordycepin and other metabolites production on various substrates. A high number of genes encoding extracellular enzymes for degradation of complex carbohydrates, lipids and proteins were existed in C. militaris genome. By comparative genome-scale analysis, the adenine metabolic pathway towards putative cordycepin biosynthesis was reconstructed, indicating their evolutionary relationships across eleven species of entomopathogenic fungi. The overall metabolic routes involved in the putative cordycepin biosynthesis were also identified in C. militaris, including central carbon metabolism, amino acid metabolism (glycine, l-glutamine and l-aspartate) and nucleotide metabolism (adenosine and adenine). Interestingly, a lack of the sequence coding for ribonucleotide reductase inhibitor was observed in C. militaris that might contribute to its over-production of cordycepin. Copyright © 2017. Published by Elsevier B.V.

Production of amino acids - Genetic and metabolic engineering approaches.

PubMed

Lee, Jin-Ho; Wendisch, Volker F

2017-12-01

The biotechnological production of amino acids occurs at the million-ton scale and annually about 6milliontons of l-glutamate and l-lysine are produced by Escherichia coli and Corynebacterium glutamicum strains. l-glutamate and l-lysine production from starch hydrolysates and molasses is very efficient and access to alternative carbon sources and new products has been enabled by metabolic engineering. This review focusses on genetic and metabolic engineering of amino acid producing strains. In particular, rational approaches involving modulation of transcriptional regulators, regulons, and attenuators will be discussed. To address current limitations of metabolic engineering, this article gives insights on recent systems metabolic engineering approaches based on functional tools and method such as genome reduction, amino acid sensors based on transcriptional regulators and riboswitches, CRISPR interference, small regulatory RNAs, DNA scaffolding, and optogenetic control, and discusses future prospects. Copyright © 2017 Elsevier Ltd. All rights reserved.

Genome-scale metabolic modeling to provide insight into the production of storage compounds during feast-famine cycles of activated sludge.

PubMed

Tajparast, Mohammad; Frigon, Dominic

2013-01-01

Studying storage metabolism during feast-famine cycles of activated sludge treatment systems provides profound insight in terms of both operational issues (e.g., foaming and bulking) and process optimization for the production of value added by-products (e.g., bioplastics). We examined the storage metabolism (including poly-β-hydroxybutyrate [PHB], glycogen, and triacylglycerols [TAGs]) during feast-famine cycles using two genome-scale metabolic models: Rhodococcus jostii RHA1 (iMT1174) and Escherichia coli K-12 (iAF1260) for growth on glucose, acetate, and succinate. The goal was to develop the proper objective function (OF) for the prediction of the main storage compound produced in activated sludge for given feast-famine cycle conditions. For the flux balance analysis, combinations of three OFs were tested. For all of them, the main OF was to maximize growth rates. Two additional sub-OFs were used: (1) minimization of biochemical fluxes, and (2) minimization of metabolic adjustments (MoMA) between the feast and famine periods. All (sub-)OFs predicted identical substrate-storage associations for the feast-famine growth of the above-mentioned metabolic models on a given substrate when glucose and acetate were set as sole carbon sources (i.e., glucose-glycogen and acetate-PHB), in agreement with experimental observations. However, in the case of succinate as substrate, the predictions depended on the network structure of the metabolic models such that the E. coli model predicted glycogen accumulation and the R. jostii model predicted PHB accumulation. While the accumulation of both PHB and glycogen was observed experimentally, PHB showed higher dynamics during an activated sludge feast-famine growth cycle with succinate as substrate. These results suggest that new modeling insights between metabolic predictions and population ecology will be necessary to properly predict metabolisms likely to emerge within the niches of activated sludge communities. Nonetheless

Metabolic engineering of yeast for production of fuels and chemicals.

PubMed

Nielsen, Jens; Larsson, Christer; van Maris, Antonius; Pronk, Jack

2013-06-01

Microbial production of fuels and chemicals from renewable carbohydrate feedstocks offers sustainable and economically attractive alternatives to their petroleum-based production. The yeast Saccharomyces cerevisiae offers many advantages as a platform cell factory for such applications. Already applied on a huge scale for bioethanol production, this yeast is easy to genetically engineer, its physiology, metabolism and genetics have been intensively studied and its robustness enables it to handle harsh industrial conditions. Introduction of novel pathways and optimization of its native cellular processes by metabolic engineering are rapidly expanding its range of cell-factory applications. Here we review recent scientific progress in metabolic engineering of S. cerevisiae for the production of bioethanol, advanced biofuels, and chemicals. Copyright © 2013 Elsevier Ltd. All rights reserved.

AlgaGEM – a genome-scale metabolic reconstruction of algae based on the Chlamydomonas reinhardtii genome

PubMed Central

2011-01-01

Background Microalgae have the potential to deliver biofuels without the associated competition for land resources. In order to realise the rates and titres necessary for commercial production, however, system-level metabolic engineering will be required. Genome scale metabolic reconstructions have revolutionized microbial metabolic engineering and are used routinely for in silico analysis and design. While genome scale metabolic reconstructions have been developed for many prokaryotes and model eukaryotes, the application to less well characterized eukaryotes such as algae is challenging not at least due to a lack of compartmentalization data. Results We have developed a genome-scale metabolic network model (named AlgaGEM) covering the metabolism for a compartmentalized algae cell based on the Chlamydomonas reinhardtii genome. AlgaGEM is a comprehensive literature-based genome scale metabolic reconstruction that accounts for the functions of 866 unique ORFs, 1862 metabolites, 2249 gene-enzyme-reaction-association entries, and 1725 unique reactions. The reconstruction was compartmentalized into the cytoplasm, mitochondrion, plastid and microbody using available data for algae complemented with compartmentalisation data for Arabidopsis thaliana. AlgaGEM describes a functional primary metabolism of Chlamydomonas and significantly predicts distinct algal behaviours such as the catabolism or secretion rather than recycling of phosphoglycolate in photorespiration. AlgaGEM was validated through the simulation of growth and algae metabolic functions inferred from literature. Using efficient resource utilisation as the optimality criterion, AlgaGEM predicted observed metabolic effects under autotrophic, heterotrophic and mixotrophic conditions. AlgaGEM predicts increased hydrogen production when cyclic electron flow is disrupted as seen in a high producing mutant derived from mutational studies. The model also predicted the physiological pathway for H2 production and

Activity affects intraspecific body-size scaling of metabolic rate in ectothermic animals.

PubMed

Glazier, Douglas Stewart

2009-10-01

Metabolic rate is commonly thought to scale with body mass (M) to the 3/4 power. However, the metabolic scaling exponent (b) may vary with activity state, as has been shown chiefly for interspecific relationships. Here I use a meta-analysis of literature data to test whether b changes with activity level within species of ectothermic animals. Data for 19 species show that b is usually higher during active exercise (mean +/- 95% confidence limits = 0.918 +/- 0.038) than during rest (0.768 +/- 0.069). This significant upward shift in b to near 1 is consistent with the metabolic level boundaries hypothesis, which predicts that maximal metabolic rate during exercise should be chiefly influenced by volume-related muscular power production (scaling as M (1)). This dependence of b on activity level does not appear to be a simple temperature effect because body temperature in ectotherms changes very little during exercise.

Metabolic Profiling of Geobacter sulfurreducens during Industrial Bioprocess Scale-Up.

PubMed

Muhamadali, Howbeer; Xu, Yun; Ellis, David I; Allwood, J William; Rattray, Nicholas J W; Correa, Elon; Alrabiah, Haitham; Lloyd, Jonathan R; Goodacre, Royston

2015-05-15

During the industrial scale-up of bioprocesses it is important to establish that the biological system has not changed significantly when moving from small laboratory-scale shake flasks or culturing bottles to an industrially relevant production level. Therefore, during upscaling of biomass production for a range of metal transformations, including the production of biogenic magnetite nanoparticles by Geobacter sulfurreducens, from 100-ml bench-scale to 5-liter fermentors, we applied Fourier transform infrared (FTIR) spectroscopy as a metabolic fingerprinting approach followed by the analysis of bacterial cell extracts by gas chromatography-mass spectrometry (GC-MS) for metabolic profiling. FTIR results clearly differentiated between the phenotypic changes associated with different growth phases as well as the two culturing conditions. Furthermore, the clustering patterns displayed by multivariate analysis were in agreement with the turbidimetric measurements, which displayed an extended lag phase for cells grown in a 5-liter bioreactor (24 h) compared to those grown in 100-ml serum bottles (6 h). GC-MS analysis of the cell extracts demonstrated an overall accumulation of fumarate during the lag phase under both culturing conditions, coinciding with the detected concentrations of oxaloacetate, pyruvate, nicotinamide, and glycerol-3-phosphate being at their lowest levels compared to other growth phases. These metabolites were overlaid onto a metabolic network of G. sulfurreducens, and taking into account the levels of these metabolites throughout the fermentation process, the limited availability of oxaloacetate and nicotinamide would seem to be the main metabolic bottleneck resulting from this scale-up process. Additional metabolite-feeding experiments were carried out to validate the above hypothesis. Nicotinamide supplementation (1 mM) did not display any significant effects on the lag phase of G. sulfurreducens cells grown in the 100-ml serum bottles. However

Next-generation genome-scale models for metabolic engineering.

PubMed

King, Zachary A; Lloyd, Colton J; Feist, Adam M; Palsson, Bernhard O

2015-12-01

Constraint-based reconstruction and analysis (COBRA) methods have become widely used tools for metabolic engineering in both academic and industrial laboratories. By employing a genome-scale in silico representation of the metabolic network of a host organism, COBRA methods can be used to predict optimal genetic modifications that improve the rate and yield of chemical production. A new generation of COBRA models and methods is now being developed--encompassing many biological processes and simulation strategies-and next-generation models enable new types of predictions. Here, three key examples of applying COBRA methods to strain optimization are presented and discussed. Then, an outlook is provided on the next generation of COBRA models and the new types of predictions they will enable for systems metabolic engineering. Copyright © 2014 Elsevier Ltd. All rights reserved.

Enumeration of Smallest Intervention Strategies in Genome-Scale Metabolic Networks

PubMed Central

von Kamp, Axel; Klamt, Steffen

2014-01-01

One ultimate goal of metabolic network modeling is the rational redesign of biochemical networks to optimize the production of certain compounds by cellular systems. Although several constraint-based optimization techniques have been developed for this purpose, methods for systematic enumeration of intervention strategies in genome-scale metabolic networks are still lacking. In principle, Minimal Cut Sets (MCSs; inclusion-minimal combinations of reaction or gene deletions that lead to the fulfilment of a given intervention goal) provide an exhaustive enumeration approach. However, their disadvantage is the combinatorial explosion in larger networks and the requirement to compute first the elementary modes (EMs) which itself is impractical in genome-scale networks. We present MCSEnumerator, a new method for effective enumeration of the smallest MCSs (with fewest interventions) in genome-scale metabolic network models. For this we combine two approaches, namely (i) the mapping of MCSs to EMs in a dual network, and (ii) a modified algorithm by which shortest EMs can be effectively determined in large networks. In this way, we can identify the smallest MCSs by calculating the shortest EMs in the dual network. Realistic application examples demonstrate that our algorithm is able to list thousands of the most efficient intervention strategies in genome-scale networks for various intervention problems. For instance, for the first time we could enumerate all synthetic lethals in E.coli with combinations of up to 5 reactions. We also applied the new algorithm exemplarily to compute strain designs for growth-coupled synthesis of different products (ethanol, fumarate, serine) by E.coli. We found numerous new engineering strategies partially requiring less knockouts and guaranteeing higher product yields (even without the assumption of optimal growth) than reported previously. The strength of the presented approach is that smallest intervention strategies can be quickly

Scaling of metabolic rate on body mass in small laboratory mammals

NASA Technical Reports Server (NTRS)

Pace, N.; Rahlmann, D. F.; Smith, A. H.

1980-01-01

The scaling of metabolic heat production rate on body mass is investigated for five species of small laboratory mammal in order to define selection of animals of metabolic rates and size range appropriate for the measurement of changes in the scaling relationship upon exposure to weightlessness in Shuttle/Spacelab experiment. Metabolic rates were measured according to oxygen consumption and carbon dioxide production for individual male and female Swiss-Webster mice, Syrian hamsters, Simonsen albino rats, Hartley guinea pigs and New Zealand white rabbits, which range in mass from 0.05 to 5 kg mature body size, at ages of 1, 2, 3, 5, 8, 12, 18 and 24 months. The metabolic intensity, defined as the heat produced per hour per kg body mass, is found to decrease dramatically with age until the animals are 6 to 8 months old, with little or no sex difference. When plotted on a logarithmic graph, the relation of metabolic rate to total body mass is found to obey a power law of index 0.676, which differs significantly from the classical value of 0.75. When the values for the mice are removed, however, an index of 0.749 is obtained. It is thus proposed that six male animals, 8 months of age, of each of the four remaining species be used to study the effects of gravitational loading on the metabolic energy requirements of terrestrial animals.

redGEM: Systematic reduction and analysis of genome-scale metabolic reconstructions for development of consistent core metabolic models

PubMed Central

Ataman, Meric

2017-01-01

Genome-scale metabolic reconstructions have proven to be valuable resources in enhancing our understanding of metabolic networks as they encapsulate all known metabolic capabilities of the organisms from genes to proteins to their functions. However the complexity of these large metabolic networks often hinders their utility in various practical applications. Although reduced models are commonly used for modeling and in integrating experimental data, they are often inconsistent across different studies and laboratories due to different criteria and detail, which can compromise transferability of the findings and also integration of experimental data from different groups. In this study, we have developed a systematic semi-automatic approach to reduce genome-scale models into core models in a consistent and logical manner focusing on the central metabolism or subsystems of interest. The method minimizes the loss of information using an approach that combines graph-based search and optimization methods. The resulting core models are shown to be able to capture key properties of the genome-scale models and preserve consistency in terms of biomass and by-product yields, flux and concentration variability and gene essentiality. The development of these “consistently-reduced” models will help to clarify and facilitate integration of different experimental data to draw new understanding that can be directly extendable to genome-scale models. PMID:28727725

Metabolic engineering of Corynebacterium glutamicum for L-arginine production.

PubMed

Park, Seok Hyun; Kim, Hyun Uk; Kim, Tae Yong; Park, Jun Seok; Kim, Suok-Su; Lee, Sang Yup

2014-08-05

L-arginine is an important amino acid for diverse industrial and health product applications. Here we report the development of metabolically engineered Corynebacterium glutamicum ATCC 21831 for the production of L-arginine. Random mutagenesis is first performed to increase the tolerance of C. glutamicum to L-arginine analogues, followed by systems metabolic engineering for further strain improvement, involving removal of regulatory repressors of arginine operon, optimization of NADPH level, disruption of L-glutamate exporter to increase L-arginine precursor and flux optimization of rate-limiting L-arginine biosynthetic reactions. Fed-batch fermentation of the final strain in 5 l and large-scale 1,500 l bioreactors allows production of 92.5 and 81.2 g l(-1) of L-arginine with the yields of 0.40 and 0.35 g L-arginine per gram carbon source (glucose plus sucrose), respectively. The systems metabolic engineering strategy described here will be useful for engineering Corynebacteria strains for the industrial production of L-arginine and related products.

MultiMetEval: Comparative and Multi-Objective Analysis of Genome-Scale Metabolic Models

PubMed Central

Gevorgyan, Albert; Kierzek, Andrzej M.; Breitling, Rainer; Takano, Eriko

2012-01-01

Comparative metabolic modelling is emerging as a novel field, supported by the development of reliable and standardized approaches for constructing genome-scale metabolic models in high throughput. New software solutions are needed to allow efficient comparative analysis of multiple models in the context of multiple cellular objectives. Here, we present the user-friendly software framework Multi-Metabolic Evaluator (MultiMetEval), built upon SurreyFBA, which allows the user to compose collections of metabolic models that together can be subjected to flux balance analysis. Additionally, MultiMetEval implements functionalities for multi-objective analysis by calculating the Pareto front between two cellular objectives. Using a previously generated dataset of 38 actinobacterial genome-scale metabolic models, we show how these approaches can lead to exciting novel insights. Firstly, after incorporating several pathways for the biosynthesis of natural products into each of these models, comparative flux balance analysis predicted that species like Streptomyces that harbour the highest diversity of secondary metabolite biosynthetic gene clusters in their genomes do not necessarily have the metabolic network topology most suitable for compound overproduction. Secondly, multi-objective analysis of biomass production and natural product biosynthesis in these actinobacteria shows that the well-studied occurrence of discrete metabolic switches during the change of cellular objectives is inherent to their metabolic network architecture. Comparative and multi-objective modelling can lead to insights that could not be obtained by normal flux balance analyses. MultiMetEval provides a powerful platform that makes these analyses straightforward for biologists. Sources and binaries of MultiMetEval are freely available from https://github.com/PiotrZakrzewski/MetEval/downloads. PMID:23272111

Computational metabolic engineering strategies for growth-coupled biofuel production by Synechocystis.

PubMed

Shabestary, Kiyan; Hudson, Elton P

2016-12-01

Chemical and fuel production by photosynthetic cyanobacteria is a promising technology but to date has not reached competitive rates and titers. Genome-scale metabolic modeling can reveal limitations in cyanobacteria metabolism and guide genetic engineering strategies to increase chemical production. Here, we used constraint-based modeling and optimization algorithms on a genome-scale model of Synechocystis PCC6803 to find ways to improve productivity of fermentative, fatty-acid, and terpene-derived fuels. OptGene and MOMA were used to find heuristics for knockout strategies that could increase biofuel productivity. OptKnock was used to find a set of knockouts that led to coupling between biofuel and growth. Our results show that high productivity of fermentation or reversed beta-oxidation derived alcohols such as 1-butanol requires elimination of NADH sinks, while terpenes and fatty-acid based fuels require creating imbalances in intracellular ATP and NADPH production and consumption. The FBA-predicted productivities of these fuels are at least 10-fold higher than those reported so far in the literature. We also discuss the physiological and practical feasibility of implementing these knockouts. This work gives insight into how cyanobacteria could be engineered to reach competitive biofuel productivities.

The large-scale organization of metabolic networks

NASA Astrophysics Data System (ADS)

Jeong, H.; Tombor, B.; Albert, R.; Oltvai, Z. N.; Barabási, A.-L.

2000-10-01

In a cell or microorganism, the processes that generate mass, energy, information transfer and cell-fate specification are seamlessly integrated through a complex network of cellular constituents and reactions. However, despite the key role of these networks in sustaining cellular functions, their large-scale structure is essentially unknown. Here we present a systematic comparative mathematical analysis of the metabolic networks of 43 organisms representing all three domains of life. We show that, despite significant variation in their individual constituents and pathways, these metabolic networks have the same topological scaling properties and show striking similarities to the inherent organization of complex non-biological systems. This may indicate that metabolic organization is not only identical for all living organisms, but also complies with the design principles of robust and error-tolerant scale-free networks, and may represent a common blueprint for the large-scale organization of interactions among all cellular constituents.

Genome scale engineering techniques for metabolic engineering.

PubMed

Liu, Rongming; Bassalo, Marcelo C; Zeitoun, Ramsey I; Gill, Ryan T

2015-11-01

Metabolic engineering has expanded from a focus on designs requiring a small number of genetic modifications to increasingly complex designs driven by advances in genome-scale engineering technologies. Metabolic engineering has been generally defined by the use of iterative cycles of rational genome modifications, strain analysis and characterization, and a synthesis step that fuels additional hypothesis generation. This cycle mirrors the Design-Build-Test-Learn cycle followed throughout various engineering fields that has recently become a defining aspect of synthetic biology. This review will attempt to summarize recent genome-scale design, build, test, and learn technologies and relate their use to a range of metabolic engineering applications. Copyright © 2015 International Metabolic Engineering Society. Published by Elsevier Inc. All rights reserved.

Improved annotation through genome-scale metabolic modeling of Aspergillus oryzae

PubMed Central

Vongsangnak, Wanwipa; Olsen, Peter; Hansen, Kim; Krogsgaard, Steen; Nielsen, Jens

2008-01-01

Background Since ancient times the filamentous fungus Aspergillus oryzae has been used in the fermentation industry for the production of fermented sauces and the production of industrial enzymes. Recently, the genome sequence of A. oryzae with 12,074 annotated genes was released but the number of hypothetical proteins accounted for more than 50% of the annotated genes. Considering the industrial importance of this fungus, it is therefore valuable to improve the annotation and further integrate genomic information with biochemical and physiological information available for this microorganism and other related fungi. Here we proposed the gene prediction by construction of an A. oryzae Expressed Sequence Tag (EST) library, sequencing and assembly. We enhanced the function assignment by our developed annotation strategy. The resulting better annotation was used to reconstruct the metabolic network leading to a genome scale metabolic model of A. oryzae. Results Our assembled EST sequences we identified 1,046 newly predicted genes in the A. oryzae genome. Furthermore, it was possible to assign putative protein functions to 398 of the newly predicted genes. Noteworthy, our annotation strategy resulted in assignment of new putative functions to 1,469 hypothetical proteins already present in the A. oryzae genome database. Using the substantially improved annotated genome we reconstructed the metabolic network of A. oryzae. This network contains 729 enzymes, 1,314 enzyme-encoding genes, 1,073 metabolites and 1,846 (1,053 unique) biochemical reactions. The metabolic reactions are compartmentalized into the cytosol, the mitochondria, the peroxisome and the extracellular space. Transport steps between the compartments and the extracellular space represent 281 reactions, of which 161 are unique. The metabolic model was validated and shown to correctly describe the phenotypic behavior of A. oryzae grown on different carbon sources. Conclusion A much enhanced annotation of the A

Metabolic Profile and Inflammatory Responses in Dairy Cows with Left Displaced Abomasum Kept under Small-Scaled Farm Conditions

PubMed Central

Klevenhusen, Fenja; Humer, Elke; Metzler-Zebeli, Barbara; Podstatzky-Lichtenstein, Leopold; Wittek, Thomas; Zebeli, Qendrim

2015-01-01

Simple Summary This research established an association between lactation number and milk production and metabolic and inflammatory responses in high-producing dairy cows affected by left abomasal displacement in small-scaled dairy farms. The study showed metabolic alterations, liver damage, and inflammation in the sick cows, which were further exacerbated with increasing lactation number and milk yield of the cows. Abstract Left displaced abomasum (LDA) is a severe metabolic disease of cattle with a strong negative impact on production efficiency of dairy farms. Metabolic and inflammatory alterations associated with this disease have been reported in earlier studies, conducted mostly in large dairy farms. This research aimed to: (1) evaluate metabolic and inflammatory responses in dairy cows affected by LDA in small-scaled dairy farms; and (2) establish an association between lactation number and milk production with the outcome of metabolic variables. The cows with LDA had lower serum calcium (Ca), but greater concentrations of non-esterified fatty acids (NEFA) and beta-hydroxy-butyrate (BHBA), in particular when lactation number was >2. Cows with LDA showed elevated levels of aspartate aminotransferase, glutamate dehydrogenase, and serum amyloid A (SAA), regardless of lactation number. In addition, this study revealed strong associations between milk yield and the alteration of metabolic profile but not with inflammation in the sick cows. Results indicate metabolic alterations, liver damage, and inflammation in LDA cows kept under small-scale farm conditions. Furthermore, the data suggest exacerbation of metabolic profile and Ca metabolism but not of inflammation and liver health with increasing lactation number and milk yield in cows affected by LDA. PMID:26479481

Environmental versatility promotes modularity in genome-scale metabolic networks.

PubMed

Samal, Areejit; Wagner, Andreas; Martin, Olivier C

2011-08-24

The ubiquity of modules in biological networks may result from an evolutionary benefit of a modular organization. For instance, modularity may increase the rate of adaptive evolution, because modules can be easily combined into new arrangements that may benefit their carrier. Conversely, modularity may emerge as a by-product of some trait. We here ask whether this last scenario may play a role in genome-scale metabolic networks that need to sustain life in one or more chemical environments. For such networks, we define a network module as a maximal set of reactions that are fully coupled, i.e., whose fluxes can only vary in fixed proportions. This definition overcomes limitations of purely graph based analyses of metabolism by exploiting the functional links between reactions. We call a metabolic network viable in a given chemical environment if it can synthesize all of an organism's biomass compounds from nutrients in this environment. An organism's metabolism is highly versatile if it can sustain life in many different chemical environments. We here ask whether versatility affects the modularity of metabolic networks. Using recently developed techniques to randomly sample large numbers of viable metabolic networks from a vast space of metabolic networks, we use flux balance analysis to study in silico metabolic networks that differ in their versatility. We find that highly versatile networks are also highly modular. They contain more modules and more reactions that are organized into modules. Most or all reactions in a module are associated with the same biochemical pathways. Modules that arise in highly versatile networks generally involve reactions that process nutrients or closely related chemicals. We also observe that the metabolism of E. coli is significantly more modular than even our most versatile networks. Our work shows that modularity in metabolic networks can be a by-product of functional constraints, e.g., the need to sustain life in multiple

Predicting growth of the healthy infant using a genome scale metabolic model.

PubMed

Nilsson, Avlant; Mardinoglu, Adil; Nielsen, Jens

2017-01-01

An estimated 165 million children globally have stunted growth, and extensive growth data are available. Genome scale metabolic models allow the simulation of molecular flux over each metabolic enzyme, and are well adapted to analyze biological systems. We used a human genome scale metabolic model to simulate the mechanisms of growth and integrate data about breast-milk intake and composition with the infant's biomass and energy expenditure of major organs. The model predicted daily metabolic fluxes from birth to age 6 months, and accurately reproduced standard growth curves and changes in body composition. The model corroborates the finding that essential amino and fatty acids do not limit growth, but that energy is the main growth limiting factor. Disruptions to the supply and demand of energy markedly affected the predicted growth, indicating that elevated energy expenditure may be detrimental. The model was used to simulate the metabolic effect of mineral deficiencies, and showed the greatest growth reduction for deficiencies in copper, iron, and magnesium ions which affect energy production through oxidative phosphorylation. The model and simulation method were integrated to a platform and shared with the research community. The growth model constitutes another step towards the complete representation of human metabolism, and may further help improve the understanding of the mechanisms underlying stunting.

Zea mays iRS1563: A Comprehensive Genome-Scale Metabolic Reconstruction of Maize Metabolism

PubMed Central

Saha, Rajib; Suthers, Patrick F.; Maranas, Costas D.

2011-01-01

The scope and breadth of genome-scale metabolic reconstructions have continued to expand over the last decade. Herein, we introduce a genome-scale model for a plant with direct applications to food and bioenergy production (i.e., maize). Maize annotation is still underway, which introduces significant challenges in the association of metabolic functions to genes. The developed model is designed to meet rigorous standards on gene-protein-reaction (GPR) associations, elementally and charged balanced reactions and a biomass reaction abstracting the relative contribution of all biomass constituents. The metabolic network contains 1,563 genes and 1,825 metabolites involved in 1,985 reactions from primary and secondary maize metabolism. For approximately 42% of the reactions direct literature evidence for the participation of the reaction in maize was found. As many as 445 reactions and 369 metabolites are unique to the maize model compared to the AraGEM model for A. thaliana. 674 metabolites and 893 reactions are present in Zea mays iRS1563 that are not accounted for in maize C4GEM. All reactions are elementally and charged balanced and localized into six different compartments (i.e., cytoplasm, mitochondrion, plastid, peroxisome, vacuole and extracellular). GPR associations are also established based on the functional annotation information and homology prediction accounting for monofunctional, multifunctional and multimeric proteins, isozymes and protein complexes. We describe results from performing flux balance analysis under different physiological conditions, (i.e., photosynthesis, photorespiration and respiration) of a C4 plant and also explore model predictions against experimental observations for two naturally occurring mutants (i.e., bm1 and bm3). The developed model corresponds to the largest and more complete to-date effort at cataloguing metabolism for a plant species. PMID:21755001

Genome-scale metabolic model of Pichia pastoris with native and humanized glycosylation of recombinant proteins.

PubMed

Irani, Zahra Azimzadeh; Kerkhoven, Eduard J; Shojaosadati, Seyed Abbas; Nielsen, Jens

2016-05-01

Pichia pastoris is used for commercial production of human therapeutic proteins, and genome-scale models of P. pastoris metabolism have been generated in the past to study the metabolism and associated protein production by this yeast. A major challenge with clinical usage of recombinant proteins produced by P. pastoris is the difference in N-glycosylation of proteins produced by humans and this yeast. However, through metabolic engineering, a P. pastoris strain capable of producing humanized N-glycosylated proteins was constructed. The current genome-scale models of P. pastoris do not address native nor humanized N-glycosylation, and we therefore developed ihGlycopastoris, an extension to the iLC915 model with both native and humanized N-glycosylation for recombinant protein production, but also an estimation of N-glycosylation of P. pastoris native proteins. This new model gives a better prediction of protein yield, demonstrates the effect of the different types of N-glycosylation of protein yield, and can be used to predict potential targets for strain improvement. The model represents a step towards a more complete description of protein production in P. pastoris, which is required for using these models to understand and optimize protein production processes. © 2015 Wiley Periodicals, Inc.

Swimming in Light: A Large-Scale Computational Analysis of the Metabolism of Dinoroseobacter shibae

PubMed Central

Rex, Rene; Bill, Nelli; Schmidt-Hohagen, Kerstin; Schomburg, Dietmar

2013-01-01

The Roseobacter clade is a ubiquitous group of marine α-proteobacteria. To gain insight into the versatile metabolism of this clade, we took a constraint-based approach and created a genome-scale metabolic model (iDsh827) of Dinoroseobacter shibae DFL12T. Our model is the first accounting for the energy demand of motility, the light-driven ATP generation and experimentally determined specific biomass composition. To cover a large variety of environmental conditions, as well as plasmid and single gene knock-out mutants, we simulated 391,560 different physiological states using flux balance analysis. We analyzed our results with regard to energy metabolism, validated them experimentally, and revealed a pronounced metabolic response to the availability of light. Furthermore, we introduced the energy demand of motility as an important parameter in genome-scale metabolic models. The results of our simulations also gave insight into the changing usage of the two degradation routes for dimethylsulfoniopropionate, an abundant compound in the ocean. A side product of dimethylsulfoniopropionate degradation is dimethyl sulfide, which seeds cloud formation and thus enhances the reflection of sunlight. By our exhaustive simulations, we were able to identify single-gene knock-out mutants, which show an increased production of dimethyl sulfide. In addition to the single-gene knock-out simulations we studied the effect of plasmid loss on the metabolism. Moreover, we explored the possible use of a functioning phosphofructokinase for D. shibae. PMID:24098096

Metabolic Engineering for the Production of Natural Products

PubMed Central

Pickens, Lauren B.; Tang, Yi; Chooi, Yit-Heng

2014-01-01

Natural products and natural product derived compounds play an important role in modern healthcare as frontline treatments for many diseases and as inspiration for chemically synthesized therapeutics. With advances in sequencing and recombinant DNA technology, many of the biosynthetic pathways responsible for the production of these chemically complex and pharmaceutically valuable compounds have been elucidated. With an ever expanding toolkit of biosynthetic components, metabolic engineering is an increasingly powerful method to improve natural product titers and generate novel compounds. Heterologous production platforms have enabled access to pathways from difficult to culture strains; systems biology and metabolic modeling tools have resulted in increasing predictive and analytic capabilities; advances in expression systems and regulation have enabled the fine-tuning of pathways for increased efficiency, and characterization of individual pathway components has facilitated the construction of hybrid pathways for the production of new compounds. These advances in the many aspects of metabolic engineering have not only yielded fascinating scientific discoveries but also make it an increasingly viable approach for the optimization of natural product biosynthesis. PMID:22432617

Synergizing 13C Metabolic Flux Analysis and Metabolic Engineering for Biochemical Production.

PubMed

Guo, Weihua; Sheng, Jiayuan; Feng, Xueyang

Metabolic engineering of industrial microorganisms to produce chemicals, fuels, and drugs has attracted increasing interest as it provides an environment-friendly and renewable route that does not depend on depleting petroleum sources. However, the microbial metabolism is so complex that metabolic engineering efforts often have difficulty in achieving a satisfactory yield, titer, or productivity of the target chemical. To overcome this challenge, 13 C Metabolic Flux Analysis ( 13 C-MFA) has been developed to investigate rigorously the cell metabolism and quantify the carbon flux distribution in central metabolic pathways. In the past decade, 13 C-MFA has been widely used in academic labs and the biotechnology industry to pinpoint the key issues related to microbial-based chemical production and to guide the development of the appropriate metabolic engineering strategies for improving the biochemical production. In this chapter we introduce the basics of 13 C-MFA and illustrate how 13 C-MFA has been applied to synergize with metabolic engineering to identify and tackle the rate-limiting steps in biochemical production.

A common scaling rule for abundance, energetics, and production of parasitic and free-living species

USGS Publications Warehouse

Hechinger, Ryan F.; Lafferty, Kevin D.; Dobson, Andy P.; Brown, James H.; Kuris, Armand M.

2011-01-01

The metabolic theory of ecology uses the scaling of metabolism with body size and temperature to explain the causes and consequences of species abundance. However, the theory and its empirical tests have never simultaneously examined parasites alongside free-living species. This is unfortunate because parasites represent at least half of species diversity. We show that metabolic scaling theory could not account for the abundance of parasitic or free-living species in three estuarine food webs until accounting for trophic dynamics. Analyses then revealed that the abundance of all species uniformly scaled with body mass to the - 3/4 power. This result indicates "production equivalence," where biomass production within trophic levels is invariant of body size across all species and functional groups: invertebrate or vertebrate, ectothermic or endothermic, and free-living or parasitic.

13C metabolic flux analysis at a genome-scale.

PubMed

Gopalakrishnan, Saratram; Maranas, Costas D

2015-11-01

Metabolic models used in 13C metabolic flux analysis generally include a limited number of reactions primarily from central metabolism. They typically omit degradation pathways, complete cofactor balances, and atom transition contributions for reactions outside central metabolism. This study addresses the impact on prediction fidelity of scaling-up mapping models to a genome-scale. The core mapping model employed in this study accounts for (75 reactions and 65 metabolites) primarily from central metabolism. The genome-scale metabolic mapping model (GSMM) (697 reaction and 595 metabolites) is constructed using as a basis the iAF1260 model upon eliminating reactions guaranteed not to carry flux based on growth and fermentation data for a minimal glucose growth medium. Labeling data for 17 amino acid fragments obtained from cells fed with glucose labeled at the second carbon was used to obtain fluxes and ranges. Metabolic fluxes and confidence intervals are estimated, for both core and genome-scale mapping models, by minimizing the sum of square of differences between predicted and experimentally measured labeling patterns using the EMU decomposition algorithm. Overall, we find that both topology and estimated values of the metabolic fluxes remain largely consistent between core and GSM model. Stepping up to a genome-scale mapping model leads to wider flux inference ranges for 20 key reactions present in the core model. The glycolysis flux range doubles due to the possibility of active gluconeogenesis, the TCA flux range expanded by 80% due to the availability of a bypass through arginine consistent with labeling data, and the transhydrogenase reaction flux was essentially unresolved due to the presence of as many as five routes for the inter-conversion of NADPH to NADH afforded by the genome-scale model. By globally accounting for ATP demands in the GSMM model the unused ATP decreased drastically with the lower bound matching the maintenance ATP requirement. A non

The scaling and temperature dependence of vertebrate metabolism

PubMed Central

White, Craig R; Phillips, Nicole F; Seymour, Roger S

2005-01-01

Body size and temperature are primary determinants of metabolic rate, and the standard metabolic rate (SMR) of animals ranging in size from unicells to mammals has been thought to be proportional to body mass (M) raised to the power of three-quarters for over 40 years. However, recent evidence from rigorously selected datasets suggests that this is not the case for birds and mammals. To determine whether the influence of body mass on the metabolic rate of vertebrates is indeed universal, we compiled SMR measurements for 938 species spanning six orders of magnitude variation in mass. When normalized to a common temperature of 38 °C, the SMR scaling exponents of fish, amphibians, reptiles, birds and mammals are significantly heterogeneous. This suggests both that there is no universal metabolic allometry and that models that attempt to explain only quarter-power scaling of metabolic rate are unlikely to succeed. PMID:17148344

Metabolic allometric scaling model: combining cellular transportation and heat dissipation constraints.

PubMed

Shestopaloff, Yuri K

2016-08-15

Living organisms need energy to be 'alive'. Energy is produced by the biochemical processing of nutrients, and the rate of energy production is called the metabolic rate. Metabolism is very important from evolutionary and ecological perspectives, and for organismal development and functioning. It depends on different parameters, of which organism mass is considered to be one of the most important. Simple relationships between the mass of organisms and their metabolic rates were empirically discovered by M. Kleiber in 1932. Such dependence is described by a power function, whose exponent is referred to as the allometric scaling coefficient. With the increase of mass, the metabolic rate usually increases more slowly; if mass increases by two times, the metabolic rate increases less than two times. This fact has far-reaching implications for the organization of life. The fundamental biological and biophysical mechanisms underlying this phenomenon are still not well understood. The present study shows that one such primary mechanism relates to transportation of substances, such as nutrients and waste, at a cellular level. Variations in cell size and associated cellular transportation costs explain the known variance of the allometric exponent. The introduced model also includes heat dissipation constraints. The model agrees with experimental observations and reconciles experimental results across different taxa. It ties metabolic scaling to organismal and environmental characteristics, helps to define perspective directions of future research and allows the prediction of allometric exponents based on characteristics of organisms and the environments they live in. © 2016. Published by The Company of Biologists Ltd.

Systems and synthetic metabolic engineering for amino acid production - the heartbeat of industrial strain development.

PubMed

Becker, Judith; Wittmann, Christoph

2012-10-01

With a world market of more than four million tons per year, l-amino acids are among the most important products in industrial biotechnology. The recent years have seen a tremendous progress in the development of tailor-made strains for such products, intensively driven from systems metabolic engineering, which upgrades strain engineering into a concept of optimization on a global scale. This concept seems especially valuable for efficient amino acid production, demanding for a global modification of pathway fluxes - a challenge with regard to the high complexity of the underlying metabolism, superimposed by various layers of metabolic and transcriptional control. Copyright © 2011 Elsevier Ltd. All rights reserved.

Optimizing eukaryotic cell hosts for protein production through systems biotechnology and genome-scale modeling.

PubMed

Gutierrez, Jahir M; Lewis, Nathan E

2015-07-01

Eukaryotic cell lines, including Chinese hamster ovary cells, yeast, and insect cells, are invaluable hosts for the production of many recombinant proteins. With the advent of genomic resources, one can now leverage genome-scale computational modeling of cellular pathways to rationally engineer eukaryotic host cells. Genome-scale models of metabolism include all known biochemical reactions occurring in a specific cell. By describing these mathematically and using tools such as flux balance analysis, the models can simulate cell physiology and provide targets for cell engineering that could lead to enhanced cell viability, titer, and productivity. Here we review examples in which metabolic models in eukaryotic cell cultures have been used to rationally select targets for genetic modification, improve cellular metabolic capabilities, design media supplementation, and interpret high-throughput omics data. As more comprehensive models of metabolism and other cellular processes are developed for eukaryotic cell culture, these will enable further exciting developments in cell line engineering, thus accelerating recombinant protein production and biotechnology in the years to come. Copyright © 2015 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Reconstruction of genome-scale human metabolic models using omics data.

PubMed

Ryu, Jae Yong; Kim, Hyun Uk; Lee, Sang Yup

2015-08-01

The impact of genome-scale human metabolic models on human systems biology and medical sciences is becoming greater, thanks to increasing volumes of model building platforms and publicly available omics data. The genome-scale human metabolic models started with Recon 1 in 2007, and have since been used to describe metabolic phenotypes of healthy and diseased human tissues and cells, and to predict therapeutic targets. Here we review recent trends in genome-scale human metabolic modeling, including various generic and tissue/cell type-specific human metabolic models developed to date, and methods, databases and platforms used to construct them. For generic human metabolic models, we pay attention to Recon 2 and HMR 2.0 with emphasis on data sources used to construct them. Draft and high-quality tissue/cell type-specific human metabolic models have been generated using these generic human metabolic models. Integration of tissue/cell type-specific omics data with the generic human metabolic models is the key step, and we discuss omics data and their integration methods to achieve this task. The initial version of the tissue/cell type-specific human metabolic models can further be computationally refined through gap filling, reaction directionality assignment and the subcellular localization of metabolic reactions. We review relevant tools for this model refinement procedure as well. Finally, we suggest the direction of further studies on reconstructing an improved human metabolic model.

Quantum metabolism explains the allometric scaling of metabolic rates.

PubMed

Demetrius, Lloyd; Tuszynski, J A

2010-03-06

A general model explaining the origin of allometric laws of physiology is proposed based on coupled energy-transducing oscillator networks embedded in a physical d-dimensional space (d = 1, 2, 3). This approach integrates Mitchell's theory of chemi-osmosis with the Debye model of the thermal properties of solids. We derive a scaling rule that relates the energy generated by redox reactions in cells, the dimensionality of the physical space and the mean cycle time. Two major regimes are found corresponding to classical and quantum behaviour. The classical behaviour leads to allometric isometry while the quantum regime leads to scaling laws relating metabolic rate and body size that cover a broad range of exponents that depend on dimensionality and specific parameter values. The regimes are consistent with a range of behaviours encountered in micelles, plants and animals and provide a conceptual framework for a theory of the metabolic function of living systems.

A genome-scale Escherichia coli kinetic metabolic model k-ecoli457 satisfying flux data for multiple mutant strains

DOE Office of Scientific and Technical Information (OSTI.GOV)

Khodayari, Ali; Maranas, Costas D.

Kinetic models of metabolism at a genome scale that faithfully recapitulate the effect of multiple genetic interventions would be transformative in our ability to reliably design novel overproducing microbial strains. Here, we introduce k-ecoli457, a genome-scale kinetic model of Escherichia coli metabolism that satisfies fluxomic data for wild-type and 25 mutant strains under different substrates and growth conditions. The k-ecoli457 model contains 457 model reactions, 337 metabolites and 295 substrate-level regulatory interactions. Parameterization is carried out using a genetic algorithm by simultaneously imposing all available fluxomic data (about 30 measured fluxes per mutant). Furthermore, the Pearson correlation coefficient between experimentalmore » data and predicted product yields for 320 engineered strains spanning 24 product metabolites is 0.84. This is substantially higher than that using flux balance analysis, minimization of metabolic adjustment or maximization of product yield exhibiting systematic errors with correlation coefficients of, respectively, 0.18, 0.37 and 0.47.« less

A genome-scale Escherichia coli kinetic metabolic model k-ecoli457 satisfying flux data for multiple mutant strains

DOE PAGES

Khodayari, Ali; Maranas, Costas D.

2016-12-20

Kinetic models of metabolism at a genome scale that faithfully recapitulate the effect of multiple genetic interventions would be transformative in our ability to reliably design novel overproducing microbial strains. Here, we introduce k-ecoli457, a genome-scale kinetic model of Escherichia coli metabolism that satisfies fluxomic data for wild-type and 25 mutant strains under different substrates and growth conditions. The k-ecoli457 model contains 457 model reactions, 337 metabolites and 295 substrate-level regulatory interactions. Parameterization is carried out using a genetic algorithm by simultaneously imposing all available fluxomic data (about 30 measured fluxes per mutant). Furthermore, the Pearson correlation coefficient between experimentalmore » data and predicted product yields for 320 engineered strains spanning 24 product metabolites is 0.84. This is substantially higher than that using flux balance analysis, minimization of metabolic adjustment or maximization of product yield exhibiting systematic errors with correlation coefficients of, respectively, 0.18, 0.37 and 0.47.« less

Metabolic heat production by human and animal populations in cities.

PubMed

Stewart, Iain D; Kennedy, Chris A

2017-07-01

Anthropogenic heating from building energy use, vehicle fuel consumption, and human metabolism is a key term in the urban energy budget equation. Heating from human metabolism, however, is often excluded from urban energy budgets because it is widely observed to be negligible. Few reports for low-latitude cities are available to support this observation, and no reports exist on the contribution of domestic animals to urban heat budgets. To provide a more comprehensive view of metabolic heating in cities, we quantified all terms of the anthropogenic heat budget at metropolitan scale for the world's 26 largest cities, using a top-down statistical approach. Results show that metabolic heat release from human populations in mid-latitude cities (e.g. London, Tokyo, New York) accounts for 4-8% of annual anthropogenic heating, compared to 10-45% in high-density tropical cities (e.g. Cairo, Dhaka, Kolkata). Heat release from animal populations amounts to <1% of anthropogenic heating in all cities. Heat flux density from human and animal metabolism combined is highest in Mumbai-the world's most densely populated megacity-at 6.5 W m -2 , surpassing heat production by electricity use in buildings (5.8 W m -2 ) and fuel combustion in vehicles (3.9 W m -2 ). These findings, along with recent output from global climate models, suggest that in the world's largest and most crowded cities, heat emissions from human metabolism alone can force measurable change in mean annual temperature at regional scale.

Metabolic heat production by human and animal populations in cities

NASA Astrophysics Data System (ADS)

Stewart, Iain D.; Kennedy, Chris A.

2017-07-01

Anthropogenic heating from building energy use, vehicle fuel consumption, and human metabolism is a key term in the urban energy budget equation. Heating from human metabolism, however, is often excluded from urban energy budgets because it is widely observed to be negligible. Few reports for low-latitude cities are available to support this observation, and no reports exist on the contribution of domestic animals to urban heat budgets. To provide a more comprehensive view of metabolic heating in cities, we quantified all terms of the anthropogenic heat budget at metropolitan scale for the world's 26 largest cities, using a top-down statistical approach. Results show that metabolic heat release from human populations in mid-latitude cities (e.g. London, Tokyo, New York) accounts for 4-8% of annual anthropogenic heating, compared to 10-45% in high-density tropical cities (e.g. Cairo, Dhaka, Kolkata). Heat release from animal populations amounts to <1% of anthropogenic heating in all cities. Heat flux density from human and animal metabolism combined is highest in Mumbai—the world's most densely populated megacity—at 6.5 W m-2, surpassing heat production by electricity use in buildings (5.8 W m-2) and fuel combustion in vehicles (3.9 W m-2). These findings, along with recent output from global climate models, suggest that in the world's largest and most crowded cities, heat emissions from human metabolism alone can force measurable change in mean annual temperature at regional scale.

Identification of metabolic engineering targets for the enhancement of 1,4-butanediol production in recombinant E. coli using large-scale kinetic models.

PubMed

Andreozzi, Stefano; Chakrabarti, Anirikh; Soh, Keng Cher; Burgard, Anthony; Yang, Tae Hoon; Van Dien, Stephen; Miskovic, Ljubisa; Hatzimanikatis, Vassily

2016-05-01

Rational metabolic engineering methods are increasingly employed in designing the commercially viable processes for the production of chemicals relevant to pharmaceutical, biotechnology, and food and beverage industries. With the growing availability of omics data and of methodologies capable to integrate the available data into models, mathematical modeling and computational analysis are becoming important in designing recombinant cellular organisms and optimizing cell performance with respect to desired criteria. In this contribution, we used the computational framework ORACLE (Optimization and Risk Analysis of Complex Living Entities) to analyze the physiology of recombinant Escherichia coli producing 1,4-butanediol (BDO) and to identify potential strategies for improved production of BDO. The framework allowed us to integrate data across multiple levels and to construct a population of large-scale kinetic models despite the lack of available information about kinetic properties of every enzyme in the metabolic pathways. We analyzed these models and we found that the enzymes that primarily control the fluxes leading to BDO production are part of central glycolysis, the lower branch of tricarboxylic acid (TCA) cycle and the novel BDO production route. Interestingly, among the enzymes between the glucose uptake and the BDO pathway, the enzymes belonging to the lower branch of TCA cycle have been identified as the most important for improving BDO production and yield. We also quantified the effects of changes of the target enzymes on other intracellular states like energy charge, cofactor levels, redox state, cellular growth, and byproduct formation. Independent earlier experiments on this strain confirmed that the computationally obtained conclusions are consistent with the experimentally tested designs, and the findings of the present studies can provide guidance for future work on strain improvement. Overall, these studies demonstrate the potential and

Genome-scale metabolic modeling of Mucor circinelloides and comparative analysis with other oleaginous species.

PubMed

Vongsangnak, Wanwipa; Klanchui, Amornpan; Tawornsamretkit, Iyarest; Tatiyaborwornchai, Witthawin; Laoteng, Kobkul; Meechai, Asawin

2016-06-01

We present a novel genome-scale metabolic model iWV1213 of Mucor circinelloides, which is an oleaginous fungus for industrial applications. The model contains 1213 genes, 1413 metabolites and 1326 metabolic reactions across different compartments. We demonstrate that iWV1213 is able to accurately predict the growth rates of M. circinelloides on various nutrient sources and culture conditions using Flux Balance Analysis and Phenotypic Phase Plane analysis. Comparative analysis of three oleaginous genome-scale models, including M. circinelloides (iWV1213), Mortierella alpina (iCY1106) and Yarrowia lipolytica (iYL619_PCP) revealed that iWV1213 possesses a higher number of genes involved in carbohydrate, amino acid, and lipid metabolisms that might contribute to its versatility in nutrient utilization. Moreover, the identification of unique and common active reactions among the Zygomycetes oleaginous models using Flux Variability Analysis unveiled a set of gene/enzyme candidates as metabolic engineering targets for cellular improvement. Thus, iWV1213 offers a powerful metabolic engineering tool for multi-level omics analysis, enabling strain optimization as a cell factory platform of lipid-based production. Copyright © 2016 Elsevier B.V. All rights reserved.

Pore-scale simulation of microbial growth using a genome-scale metabolic model: Implications for Darcy-scale reactive transport

NASA Astrophysics Data System (ADS)

Tartakovsky, G. D.; Tartakovsky, A. M.; Scheibe, T. D.; Fang, Y.; Mahadevan, R.; Lovley, D. R.

2013-09-01

Recent advances in microbiology have enabled the quantitative simulation of microbial metabolism and growth based on genome-scale characterization of metabolic pathways and fluxes. We have incorporated a genome-scale metabolic model of the iron-reducing bacteria Geobacter sulfurreducens into a pore-scale simulation of microbial growth based on coupling of iron reduction to oxidation of a soluble electron donor (acetate). In our model, fluid flow and solute transport is governed by a combination of the Navier-Stokes and advection-diffusion-reaction equations. Microbial growth occurs only on the surface of soil grains where solid-phase mineral iron oxides are available. Mass fluxes of chemical species associated with microbial growth are described by the genome-scale microbial model, implemented using a constraint-based metabolic model, and provide the Robin-type boundary condition for the advection-diffusion equation at soil grain surfaces. Conventional models of microbially-mediated subsurface reactions use a lumped reaction model that does not consider individual microbial reaction pathways, and describe reactions rates using empirically-derived rate formulations such as the Monod-type kinetics. We have used our pore-scale model to explore the relationship between genome-scale metabolic models and Monod-type formulations, and to assess the manifestation of pore-scale variability (microenvironments) in terms of apparent Darcy-scale microbial reaction rates. The genome-scale model predicted lower biomass yield, and different stoichiometry for iron consumption, in comparison to prior Monod formulations based on energetics considerations. We were able to fit an equivalent Monod model, by modifying the reaction stoichiometry and biomass yield coefficient, that could effectively match results of the genome-scale simulation of microbial behaviors under excess nutrient conditions, but predictions of the fitted Monod model deviated from those of the genome-scale model

Pore-scale simulation of microbial growth using a genome-scale metabolic model: Implications for Darcy-scale reactive transport

DOE Office of Scientific and Technical Information (OSTI.GOV)

Tartakovsky, Guzel D.; Tartakovsky, Alexandre M.; Scheibe, Timothy D.

2013-09-07

Recent advances in microbiology have enabled the quantitative simulation of microbial metabolism and growth based on genome-scale characterization of metabolic pathways and fluxes. We have incorporated a genome-scale metabolic model of the iron-reducing bacteria Geobacter sulfurreducens into a pore-scale simulation of microbial growth based on coupling of iron reduction to oxidation of a soluble electron donor (acetate). In our model, fluid flow and solute transport is governed by a combination of the Navier-Stokes and advection-diffusion-reaction equations. Microbial growth occurs only on the surface of soil grains where solid-phase mineral iron oxides are available. Mass fluxes of chemical species associated withmore » microbial growth are described by the genome-scale microbial model, implemented using a constraint-based metabolic model, and provide the Robin-type boundary condition for the advection-diffusion equation at soil grain surfaces. Conventional models of microbially-mediated subsurface reactions use a lumped reaction model that does not consider individual microbial reaction pathways, and describe reactions rates using empirically-derived rate formulations such as the Monod-type kinetics. We have used our pore-scale model to explore the relationship between genome-scale metabolic models and Monod-type formulations, and to assess the manifestation of pore-scale variability (microenvironments) in terms of apparent Darcy-scale microbial reaction rates. The genome-scale model predicted lower biomass yield, and different stoichiometry for iron consumption, in comparisonto prior Monod formulations based on energetics considerations. We were able to fit an equivalent Monod model, by modifying the reaction stoichiometry and biomass yield coefficient, that could effectively match results of the genome-scale simulation of microbial behaviors under excess nutrient conditions, but predictions of the fitted Monod model deviated from those of the genome-scale

Pore-scale simulation of microbial growth using a genome-scale metabolic model: Implications for Darcy-scale reactive transport

NASA Astrophysics Data System (ADS)

Scheibe, T. D.; Tartakovsky, G.; Tartakovsky, A. M.; Fang, Y.; Mahadevan, R.; Lovley, D. R.

2012-12-01

Recent advances in microbiology have enabled the quantitative simulation of microbial metabolism and growth based on genome-scale characterization of metabolic pathways and fluxes. We have incorporated a genome-scale metabolic model of the iron-reducing bacteria Geobacter sulfurreducens into a pore-scale simulation of microbial growth based on coupling of iron reduction to oxidation of a soluble electron donor (acetate). In our model, fluid flow and solute transport is governed by a combination of the Navier-Stokes and advection-diffusion-reaction equations. Microbial growth occurs only on the surface of soil grains where solid-phase mineral iron oxides are available. Mass fluxes of chemical species associated with microbial growth are described by the genome-scale microbial model, implemented using a constraint-based metabolic model, and provide the Robin-type boundary condition for the advection-diffusion equation at soil grain surfaces. Conventional models of microbially-mediated subsurface reactions use a lumped reaction model that does not consider individual microbial reaction pathways, and describe reactions rates using empirically-derived rate formulations such as the Monod-type kinetics. We have used our pore-scale model to explore the relationship between genome-scale metabolic models and Monod-type formulations, and to assess the manifestation of pore-scale variability (microenvironments) in terms of apparent Darcy-scale microbial reaction rates. The genome-scale model predicted lower biomass yield, and different stoichiometry for iron consumption, in comparison to prior Monod formulations based on energetics considerations. We were able to fit an equivalent Monod model, by modifying the reaction stoichiometry and biomass yield coefficient, that could effectively match results of the genome-scale simulation of microbial behaviors under excess nutrient conditions, but predictions of the fitted Monod model deviated from those of the genome-scale model

Investigating host-pathogen behavior and their interaction using genome-scale metabolic network models.

PubMed

Sadhukhan, Priyanka P; Raghunathan, Anu

2014-01-01

Genome Scale Metabolic Modeling methods represent one way to compute whole cell function starting from the genome sequence of an organism and contribute towards understanding and predicting the genotype-phenotype relationship. About 80 models spanning all the kingdoms of life from archaea to eukaryotes have been built till date and used to interrogate cell phenotype under varying conditions. These models have been used to not only understand the flux distribution in evolutionary conserved pathways like glycolysis and the Krebs cycle but also in applications ranging from value added product formation in Escherichia coli to predicting inborn errors of Homo sapiens metabolism. This chapter describes a protocol that delineates the process of genome scale metabolic modeling for analysing host-pathogen behavior and interaction using flux balance analysis (FBA). The steps discussed in the process include (1) reconstruction of a metabolic network from the genome sequence, (2) its representation in a precise mathematical framework, (3) its translation to a model, and (4) the analysis using linear algebra and optimization. The methods for biological interpretations of computed cell phenotypes in the context of individual host and pathogen models and their integration are also discussed.

FindPrimaryPairs: An efficient algorithm for predicting element-transferring reactant/product pairs in metabolic networks.

PubMed

Steffensen, Jon Lund; Dufault-Thompson, Keith; Zhang, Ying

2018-01-01

The metabolism of individual organisms and biological communities can be viewed as a network of metabolites connected to each other through chemical reactions. In metabolic networks, chemical reactions transform reactants into products, thereby transferring elements between these metabolites. Knowledge of how elements are transferred through reactant/product pairs allows for the identification of primary compound connections through a metabolic network. However, such information is not readily available and is often challenging to obtain for large reaction databases or genome-scale metabolic models. In this study, a new algorithm was developed for automatically predicting the element-transferring reactant/product pairs using the limited information available in the standard representation of metabolic networks. The algorithm demonstrated high efficiency in analyzing large datasets and provided accurate predictions when benchmarked with manually curated data. Applying the algorithm to the visualization of metabolic networks highlighted pathways of primary reactant/product connections and provided an organized view of element-transferring biochemical transformations. The algorithm was implemented as a new function in the open source software package PSAMM in the release v0.30 (https://zhanglab.github.io/psamm/).

Genome-scale modeling of human metabolism - a systems biology approach.

PubMed

Mardinoglu, Adil; Gatto, Francesco; Nielsen, Jens

2013-09-01

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. Copyright © 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Probing the genome-scale metabolic landscape of Bordetella pertussis, the causative agent of whooping cough.

PubMed

Branco Dos Santos, Filipe; Olivier, Brett G; Boele, Joost; Smessaert, Vincent; De Rop, Philippe; Krumpochova, Petra; Klau, Gunnar W; Giera, Martin; Dehottay, Philippe; Teusink, Bas; Goffin, Philippe

2017-08-25

Whooping cough is a highly-contagious respiratory disease caused by Bordetella pertussi s. Despite vaccination, its incidence has been rising alarmingly, and yet, the physiology of B. pertussis remains poorly understood. We combined genome-scale metabolic reconstruction, a novel optimization algorithm and experimental data to probe the full metabolic potential of this pathogen, using strain Tohama I as a reference. Experimental validation showed that B. pertussis secretes a significant proportion of nitrogen as arginine and purine nucleosides, which may contribute to modulation of the host response. We also found that B. pertussis can be unexpectedly versatile, being able to metabolize many compounds while displaying minimal nutrient requirements. It can grow without cysteine - using inorganic sulfur sources such as thiosulfate - and it can grow on organic acids such as citrate or lactate as sole carbon sources, providing in vivo demonstration that its TCA cycle is functional. Although the metabolic reconstruction of eight additional strains indicates that the structural genes underlying this metabolic flexibility are widespread, experimental validation suggests a role of strain-specific regulatory mechanisms in shaping metabolic capabilities. Among five alternative strains tested, three were shown to grow on substrate combinations requiring a functional TCA cycle, but only one could use thiosulfate. Finally, the metabolic model was used to rationally design growth media with over two-fold improvements in pertussis toxin production. This study thus provides novel insights into B. pertussis physiology, and highlights the potential, but also limitations of models solely based on metabolic gene content. IMPORTANCE The metabolic capabilities of Bordetella pertussis - the causative agent of whooping cough - were investigated from a systems-level perspective. We constructed a comprehensive genome-scale metabolic model for B. pertussis , and challenged its predictions

Probing the Genome-Scale Metabolic Landscape of Bordetella pertussis, the Causative Agent of Whooping Cough

PubMed Central

Olivier, Brett G.; Boele, Joost; Smessaert, Vincent; De Rop, Philippe; Krumpochova, Petra; Klau, Gunnar W.; Giera, Martin; Dehottay, Philippe; Goffin, Philippe

2017-01-01

ABSTRACT Whooping cough is a highly contagious respiratory disease caused by Bordetella pertussis. Despite widespread vaccination, its incidence has been rising alarmingly, and yet, the physiology of B. pertussis remains poorly understood. We combined genome-scale metabolic reconstruction, a novel optimization algorithm, and experimental data to probe the full metabolic potential of this pathogen, using B. pertussis strain Tohama I as a reference. Experimental validation showed that B. pertussis secretes a significant proportion of nitrogen as arginine and purine nucleosides, which may contribute to modulation of the host response. We also found that B. pertussis can be unexpectedly versatile, being able to metabolize many compounds while displaying minimal nutrient requirements. It can grow without cysteine, using inorganic sulfur sources, such as thiosulfate, and it can grow on organic acids, such as citrate or lactate, as sole carbon sources, providing in vivo demonstration that its tricarboxylic acid (TCA) cycle is functional. Although the metabolic reconstruction of eight additional strains indicates that the structural genes underlying this metabolic flexibility are widespread, experimental validation suggests a role of strain-specific regulatory mechanisms in shaping metabolic capabilities. Among five alternative strains tested, three strains were shown to grow on substrate combinations requiring a functional TCA cycle, but only one strain could use thiosulfate. Finally, the metabolic model was used to rationally design growth media with >2-fold improvements in pertussis toxin production. This study thus provides novel insights into B. pertussis physiology and highlights the potential, but also the limitations, of models based solely on metabolic gene content. IMPORTANCE The metabolic capabilities of Bordetella pertussis, the causative agent of whooping cough, were investigated from a systems-level perspective. We constructed a comprehensive genome-scale

Comprehensive analysis of a Metabolic Model for lipid production in Rhodosporidium toruloides.

PubMed

Castañeda, María Teresita; Nuñez, Sebastián; Garelli, Fabricio; Voget, Claudio; Battista, Hernán De

2018-05-19

The yeast Rhodosporidium toruloides has been extensively studied for its application in biolipid production. The knowledge of its metabolism capabilities and the application of constraint-based flux analysis methodology provide useful information for process prediction and optimization. The accuracy of the resulting predictions is highly dependent on metabolic models. A metabolic reconstruction for R. toruloides metabolism has been recently published. On the basis of this model, we developed a curated version that unblocks the central nitrogen metabolism and, in addition, completes charge and mass balances in some reactions neglected in the former model. Then, a comprehensive analysis of network capability was performed with the curated model and compared with the published metabolic reconstruction. The flux distribution obtained by lipid optimization with Flux Balance Analysis was able to replicate the internal biochemical changes that lead to lipogenesis in oleaginous microorganisms. These results motivate the development of a genome-scale model for complete elucidation of R. toruloides metabolism. Copyright © 2018 Elsevier B.V. All rights reserved.

The scaling of maximum and basal metabolic rates of mammals and birds

NASA Astrophysics Data System (ADS)

Barbosa, Lauro A.; Garcia, Guilherme J. M.; da Silva, Jafferson K. L.

2006-01-01

Allometric scaling is one of the most pervasive laws in biology. Its origin, however, is still a matter of dispute. Recent studies have established that maximum metabolic rate scales with an exponent larger than that found for basal metabolism. This unpredicted result sets a challenge that can decide which of the concurrent hypotheses is the correct theory. Here, we show that both scaling laws can be deduced from a single network model. Besides the 3/4-law for basal metabolism, the model predicts that maximum metabolic rate scales as M, maximum heart rate as M, and muscular capillary density as M, in agreement with data.

Northern Florida reef tract benthic metabolism scaled by remote sensing

USGS Publications Warehouse

Brock, J.C.; Yates, K.K.; Halley, R.B.; Kuffner, I.B.; Wright, C.W.; Hatcher, B.G.

2006-01-01

Holistic rates of excess organic carbon production (E) and calcification for a 0.5 km2 segment of the backreef platform of the northern Florida reef tract (NFRT) were estimated by combining biotope mapping using remote sensing with community metabolic rates determined with a benthic incubation system. The use of ASTER multispectral satellite imaging for the spatial scaling of benthic metabolic processes resulted in errors in E and net calcification (G) of 48 and 431% respectively, relative to estimates obtained using AISA hyperspectral airborne scanning. At 19 and 125%, the E and G errors relative to the AISA-based estimates were less pronounced for an analysis that used IKONOS multispectral satellite imagery to spatially extrapolate the chamber process measurements. Our scaling analysis indicates that the holistic calcification rate of the backreef platform of the northern Florida reef tract is negligible at 0.07 g CaCO3 m-2 d-1. All of the mapped biotopes in this reef zone are net heterotrophic, resulting in an estimated holistic excess production rate of -0.56 g C m-2 d-1, and an overall gross primary production to respiration ratio of 0.85. Based on our finding of ubiquitous heterotrophy, we infer that the backreef platform of the NFRT is a sink for external inputs of suspended particulate organic matter. Further, our results suggest that the inward advection of inorganic nutrients is not a dominant forcing mechanism for benthic biogeochemical function in the NFRT. We suggest that the degradation of the northern Florida reef tract may parallel the community phase shifts documented within other reef systems polluted by organic detritus.

Ontogenetic body-mass scaling of resting metabolic rate covaries with species-specific metabolic level and body size in spiders and snakes.

PubMed

Glazier, Douglas S

2009-08-01

According to common belief, metabolic rate usually scales with body mass to the 3/4-power, which is considered by some to be a universal law of nature. However, substantial variation in the metabolic scaling exponent (b) exists, much of which can be related to the overall metabolic level (L) of various taxonomic groups of organisms, as predicted by the recently proposed metabolic-level boundaries (MLB) hypothesis. Here the MLB hypothesis was tested using data for intraspecific (ontogenetic) body-mass scaling of resting metabolic rate in spiders and boid snakes. As predicted, in both animal groups b varies mostly between 2/3 and 1, and is significantly negatively related to L. L is, in turn, negatively related to species-specific body mass (M(m): estimated as the mass at the midpoint of a scaling relationship), and as a result, larger species tend to have steeper metabolic scaling slopes (b) than smaller species. After adjusting for the effects of M(m), b and L are still negatively related, though significantly only in the spiders, which exhibit a much wider range of L than the snakes. Therefore, in spiders and snakes the intraspecific scaling of metabolic rate with body mass itself scales with interspecific variation in both metabolic level and body mass.

Shape shifting predicts ontogenetic changes in metabolic scaling in diverse aquatic invertebrates

PubMed Central

Glazier, Douglas S.; Hirst, Andrew G.; Atkinson, David

2015-01-01

Metabolism fuels all biological activities, and thus understanding its variation is fundamentally important. Much of this variation is related to body size, which is commonly believed to follow a 3/4-power scaling law. However, during ontogeny, many kinds of animals and plants show marked shifts in metabolic scaling that deviate from 3/4-power scaling predicted by general models. Here, we show that in diverse aquatic invertebrates, ontogenetic shifts in the scaling of routine metabolic rate from near isometry (bR = scaling exponent approx. 1) to negative allometry (bR < 1), or the reverse, are associated with significant changes in body shape (indexed by bL = the scaling exponent of the relationship between body mass and body length). The observed inverse correlations between bR and bL are predicted by metabolic scaling theory that emphasizes resource/waste fluxes across external body surfaces, but contradict theory that emphasizes resource transport through internal networks. Geometric estimates of the scaling of surface area (SA) with body mass (bA) further show that ontogenetic shifts in bR and bA are positively correlated. These results support new metabolic scaling theory based on SA influences that may be applied to ontogenetic shifts in bR shown by many kinds of animals and plants. PMID:25652833

Multi-scale exploration of the technical, economic, and environmental dimensions of bio-based chemical production.

PubMed

Zhuang, Kai H; Herrgård, Markus J

2015-09-01

In recent years, bio-based chemicals have gained traction as a sustainable alternative to petrochemicals. However, despite rapid advances in metabolic engineering and synthetic biology, there remain significant economic and environmental challenges. In order to maximize the impact of research investment in a new bio-based chemical industry, there is a need for assessing the technological, economic, and environmental potentials of combinations of biomass feedstocks, biochemical products, bioprocess technologies, and metabolic engineering approaches in the early phase of development of cell factories. To address this issue, we have developed a comprehensive Multi-scale framework for modeling Sustainable Industrial Chemicals production (MuSIC), which integrates modeling approaches for cellular metabolism, bioreactor design, upstream/downstream processes and economic impact assessment. We demonstrate the use of the MuSIC framework in a case study where two major polymer precursors (1,3-propanediol and 3-hydroxypropionic acid) are produced from two biomass feedstocks (corn-based glucose and soy-based glycerol) through 66 proposed biosynthetic pathways in two host organisms (Escherichia coli and Saccharomyces cerevisiae). The MuSIC framework allows exploration of tradeoffs and interactions between economy-scale objectives (e.g. profit maximization, emission minimization), constraints (e.g. land-use constraints) and process- and cell-scale technology choices (e.g. strain design or oxygenation conditions). We demonstrate that economy-scale assessment can be used to guide specific strain design decisions in metabolic engineering, and that these design decisions can be affected by non-intuitive dependencies across multiple scales. Copyright © 2015 International Metabolic Engineering Society. Published by Elsevier Inc. All rights reserved.

Modeling cancer metabolism on a genome scale

PubMed Central

Yizhak, Keren; Chaneton, Barbara; Gottlieb, Eyal; Ruppin, Eytan

2015-01-01

Cancer cells have fundamentally altered cellular metabolism that is associated with their tumorigenicity and malignancy. In addition to the widely studied Warburg effect, several new key metabolic alterations in cancer have been established over the last decade, leading to the recognition that altered tumor metabolism is one of the hallmarks of cancer. Deciphering the full scope and functional implications of the dysregulated metabolism in cancer requires both the advancement of a variety of omics measurements and the advancement of computational approaches for the analysis and contextualization of the accumulated data. Encouragingly, while the metabolic network is highly interconnected and complex, it is at the same time probably the best characterized cellular network. Following, this review discusses the challenges that genome-scale modeling of cancer metabolism has been facing. We survey several recent studies demonstrating the first strides that have been done, testifying to the value of this approach in portraying a network-level view of the cancer metabolism and in identifying novel drug targets and biomarkers. Finally, we outline a few new steps that may further advance this field. PMID:26130389

Patchoulol Production with Metabolically Engineered Corynebacterium glutamicum

PubMed Central

Wichmann, Julian; Baier, Thomas; Frohwitter, Jonas; Risse, Joe M.; Peters-Wendisch, Petra; Kruse, Olaf

2018-01-01

Patchoulol is a sesquiterpene alcohol and an important natural product for the perfume industry. Corynebacterium glutamicum is the prominent host for the fermentative production of amino acids with an average annual production volume of ~6 million tons. Due to its robustness and well established large-scale fermentation, C. glutamicum has been engineered for the production of a number of value-added compounds including terpenoids. Both C40 and C50 carotenoids, including the industrially relevant astaxanthin, and short-chain terpenes such as the sesquiterpene valencene can be produced with this organism. In this study, systematic metabolic engineering enabled construction of a patchoulol producing C. glutamicum strain by applying the following strategies: (i) construction of a farnesyl pyrophosphate-producing platform strain by combining genomic deletions with heterologous expression of ispA from Escherichia coli; (ii) prevention of carotenoid-like byproduct formation; (iii) overproduction of limiting enzymes from the 2-c-methyl-d-erythritol 4-phosphate (MEP)-pathway to increase precursor supply; and (iv) heterologous expression of the plant patchoulol synthase gene PcPS from Pogostemon cablin. Additionally, a proof of principle liter-scale fermentation with a two-phase organic overlay-culture medium system for terpenoid capture was performed. To the best of our knowledge, the patchoulol titers demonstrated here are the highest reported to date with up to 60 mg L−1 and volumetric productivities of up to 18 mg L−1 d−1. PMID:29673223

The RAVEN Toolbox and Its Use for Generating a Genome-scale Metabolic Model for Penicillium chrysogenum

PubMed Central

Agren, Rasmus; Liu, Liming; Shoaie, Saeed; Vongsangnak, Wanwipa; Nookaew, Intawat; Nielsen, Jens

2013-01-01

We present the RAVEN (Reconstruction, Analysis and Visualization of Metabolic Networks) Toolbox: a software suite that allows for semi-automated reconstruction of genome-scale models. It makes use of published models and/or the KEGG database, coupled with extensive gap-filling and quality control features. The software suite also contains methods for visualizing simulation results and omics data, as well as a range of methods for performing simulations and analyzing the results. The software is a useful tool for system-wide data analysis in a metabolic context and for streamlined reconstruction of metabolic networks based on protein homology. The RAVEN Toolbox workflow was applied in order to reconstruct a genome-scale metabolic model for the important microbial cell factory Penicillium chrysogenum Wisconsin54-1255. The model was validated in a bibliomic study of in total 440 references, and it comprises 1471 unique biochemical reactions and 1006 ORFs. It was then used to study the roles of ATP and NADPH in the biosynthesis of penicillin, and to identify potential metabolic engineering targets for maximization of penicillin production. PMID:23555215

Improving the phenotype predictions of a yeast genome-scale metabolic model by incorporating enzymatic constraints

DOE PAGES

Sánchez, Benjamín J.; Zhang, Cheng; Nilsson, Avlant; ...

2017-03-08

Genome-scale metabolic models (GEMs) are widely used to calculate metabolic phenotypes. They rely on defining a set of constraints, the most common of which is that the production of metabolites and/or growth are limited by the carbon source uptake rate. However, enzyme abundances and kinetics, which act as limitations on metabolic fluxes, are not taken into account. Here, we present GECKO, a method that enhances a GEM to account for enzymes as part of reactions, thereby ensuring that each metabolic flux does not exceed its maximum capacity, equal to the product of the enzyme's abundance and turnover number. We appliedmore » GECKO to a Saccharomyces cerevisiae GEM and demonstrated that the new model could correctly describe phenotypes that the previous model could not, particularly under high enzymatic pressure conditions, such as yeast growing on different carbon sources in excess, coping with stress, or overexpressing a specific pathway. GECKO also allows to directly integrate quantitative proteomics data; by doing so, we significantly reduced flux variability of the model, in over 60% of metabolic reactions. Additionally, the model gives insight into the distribution of enzyme usage between and within metabolic pathways. The developed method and model are expected to increase the use of model-based design in metabolic engineering.« less

Improving the phenotype predictions of a yeast genome-scale metabolic model by incorporating enzymatic constraints

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sánchez, Benjamín J.; Zhang, Cheng; Nilsson, Avlant

Genome-scale metabolic models (GEMs) are widely used to calculate metabolic phenotypes. They rely on defining a set of constraints, the most common of which is that the production of metabolites and/or growth are limited by the carbon source uptake rate. However, enzyme abundances and kinetics, which act as limitations on metabolic fluxes, are not taken into account. Here, we present GECKO, a method that enhances a GEM to account for enzymes as part of reactions, thereby ensuring that each metabolic flux does not exceed its maximum capacity, equal to the product of the enzyme's abundance and turnover number. We appliedmore » GECKO to a Saccharomyces cerevisiae GEM and demonstrated that the new model could correctly describe phenotypes that the previous model could not, particularly under high enzymatic pressure conditions, such as yeast growing on different carbon sources in excess, coping with stress, or overexpressing a specific pathway. GECKO also allows to directly integrate quantitative proteomics data; by doing so, we significantly reduced flux variability of the model, in over 60% of metabolic reactions. Additionally, the model gives insight into the distribution of enzyme usage between and within metabolic pathways. The developed method and model are expected to increase the use of model-based design in metabolic engineering.« less

Phenotypic plasticity in the scaling of avian basal metabolic rate

PubMed Central

McKechnie, Andrew E; Freckleton, Robert P; Jetz, Walter

2006-01-01

Many birds exhibit short-term, reversible adjustments in basal metabolic rate (BMR), but the overall contribution of phenotypic plasticity to avian metabolic diversity remains unclear. The available BMR data include estimates from birds living in natural environments and captive-raised birds in more homogenous, artificial environments. All previous analyses of interspecific variation in BMR have pooled these data. We hypothesized that phenotypic plasticity is an important contributor to interspecific variation in avian BMR, and that captive-raised populations exhibit general differences in BMR compared to wild-caught populations. We tested this hypothesis by fitting general linear models to BMR data for 231 bird species, using the generalized least-squares approach to correct for phylogenetic relatedness when necessary. The scaling exponent relating BMR to body mass in captive-raised birds (0.670) was significantly shallower than in wild-caught birds (0.744). The differences in metabolic scaling between captive-raised and wild-caught birds persisted when migratory tendency and habitat aridity were controlled for. Our results reveal that phenotypic plasticity is a major contributor to avian interspecific metabolic variation. The finding that metabolic scaling in birds is partly determined by environmental factors provides further support for models that predict variation in scaling exponents, such as the allometric cascade model. PMID:16627278

On the thermodynamic origin of metabolic scaling.

PubMed

Ballesteros, Fernando J; Martinez, Vicent J; Luque, Bartolo; Lacasa, Lucas; Valor, Enric; Moya, Andrés

2018-01-23

The origin and shape of metabolic scaling has been controversial since Kleiber found that basal metabolic rate of animals seemed to vary as a power law of their body mass with exponent 3/4, instead of 2/3, as a surface-to-volume argument predicts. The universality of exponent 3/4 -claimed in terms of the fractal properties of the nutrient network- has recently been challenged according to empirical evidence that observed a wealth of robust exponents deviating from 3/4. Here we present a conceptually simple thermodynamic framework, where the dependence of metabolic rate with body mass emerges from a trade-off between the energy dissipated as heat and the energy efficiently used by the organism to maintain its metabolism. This balance tunes the shape of an additive model from which different effective scalings can be recovered as particular cases, thereby reconciling previously inconsistent empirical evidence in mammals, birds, insects and even plants under a unified framework. This model is biologically motivated, fits remarkably well the data, and also explains additional features such as the relation between energy lost as heat and mass, the role and influence of different climatic environments or the difference found between endotherms and ectotherms.

Intramolecular stable isotope distributions detect plant metabolic responses on century time scales

NASA Astrophysics Data System (ADS)

Schleucher, Jürgen; Ehlers, Ina; Augusti, Angela; Betson, Tatiana

2014-05-01

Plants respond to environmental changes on a vast range of time scales, and plant gas exchanges constitute important feedback mechanisms in the global C cycle. Responses on time scales of decades to centuries are most important for climate models, for prediction of crop productivity, and for adaptation to climate change. Unfortunately, responses on these timescale are least understood. We argue that the knowledge gap on intermediate time scales is due to a lack of adequate methods that can bridge between short-term manipulative experiments (e.g. FACE) and paleo research. Manipulative experiments in plant ecophysiology give information on metabolism on time scales up to years. However, this information cannot be linked to results from retrospective studies in paleo research, because little metabolic information can be derived from paleo archives. Stable isotopes are prominent tools in plant ecophysiology, biogeochemistry and in paleo research, but in all applications to date, isotope ratios of whole molecules are measured. However, it is well established that stable isotope abundance varies among intramolecular groups of biochemical metabolites, that is each so-called "isotopomer" has a distinct abundance. This intramolecular variation carries information on metabolic regulation, which can even be traced to individual enzymes (Schleucher et al., Plant, Cell Environ 1999). Here, we apply intramolecular isotope distributions to study the metabolic response of plants to increasing atmospheric [CO2] during the past century. Greenhouse experiments show that the deuterium abundance among the two positions in the C6H2 group of photosynthetic glucose depends on [CO2] during growth. This is observed for all plants using C3 photosynthesis, and reflects the metabolic flux ratio between photorespiration and photosynthesis. Photorespiration is a major C flux that limits assimilation in C3 plants, which encompass the overwhelming fraction of terrestrial photosynthesis and the

MEMOSys: Bioinformatics platform for genome-scale metabolic models

PubMed Central

2011-01-01

Background Recent advances in genomic sequencing have enabled the use of genome sequencing in standard biological and biotechnological research projects. The challenge is how to integrate the large amount of data in order to gain novel biological insights. One way to leverage sequence data is to use genome-scale metabolic models. We have therefore designed and implemented a bioinformatics platform which supports the development of such metabolic models. Results MEMOSys (MEtabolic MOdel research and development System) is a versatile platform for the management, storage, and development of genome-scale metabolic models. It supports the development of new models by providing a built-in version control system which offers access to the complete developmental history. Moreover, the integrated web board, the authorization system, and the definition of user roles allow collaborations across departments and institutions. Research on existing models is facilitated by a search system, references to external databases, and a feature-rich comparison mechanism. MEMOSys provides customizable data exchange mechanisms using the SBML format to enable analysis in external tools. The web application is based on the Java EE framework and offers an intuitive user interface. It currently contains six annotated microbial metabolic models. Conclusions We have developed a web-based system designed to provide researchers a novel application facilitating the management and development of metabolic models. The system is freely available at http://www.icbi.at/MEMOSys. PMID:21276275

Genome-Scale Reconstruction of the Human Astrocyte Metabolic Network

PubMed Central

Martín-Jiménez, Cynthia A.; Salazar-Barreto, Diego; Barreto, George E.; González, Janneth

2017-01-01

Astrocytes are the most abundant cells of the central nervous system; they have a predominant role in maintaining brain metabolism. In this sense, abnormal metabolic states have been found in different neuropathological diseases. Determination of metabolic states of astrocytes is difficult to model using current experimental approaches given the high number of reactions and metabolites present. Thus, genome-scale metabolic networks derived from transcriptomic data can be used as a framework to elucidate how astrocytes modulate human brain metabolic states during normal conditions and in neurodegenerative diseases. We performed a Genome-Scale Reconstruction of the Human Astrocyte Metabolic Network with the purpose of elucidating a significant portion of the metabolic map of the astrocyte. This is the first global high-quality, manually curated metabolic reconstruction network of a human astrocyte. It includes 5,007 metabolites and 5,659 reactions distributed among 8 cell compartments, (extracellular, cytoplasm, mitochondria, endoplasmic reticle, Golgi apparatus, lysosome, peroxisome and nucleus). Using the reconstructed network, the metabolic capabilities of human astrocytes were calculated and compared both in normal and ischemic conditions. We identified reactions activated in these two states, which can be useful for understanding the astrocytic pathways that are affected during brain disease. Additionally, we also showed that the obtained flux distributions in the model, are in accordance with literature-based findings. Up to date, this is the most complete representation of the human astrocyte in terms of inclusion of genes, proteins, reactions and metabolic pathways, being a useful guide for in-silico analysis of several metabolic behaviors of the astrocyte during normal and pathologic states. PMID:28243200

Shape shifting predicts ontogenetic changes in metabolic scaling in diverse aquatic invertebrates.

PubMed

Glazier, Douglas S; Hirst, Andrew G; Atkinson, David

2015-03-07

Metabolism fuels all biological activities, and thus understanding its variation is fundamentally important. Much of this variation is related to body size, which is commonly believed to follow a 3/4-power scaling law. However, during ontogeny, many kinds of animals and plants show marked shifts in metabolic scaling that deviate from 3/4-power scaling predicted by general models. Here, we show that in diverse aquatic invertebrates, ontogenetic shifts in the scaling of routine metabolic rate from near isometry (bR = scaling exponent approx. 1) to negative allometry (bR < 1), or the reverse, are associated with significant changes in body shape (indexed by bL = the scaling exponent of the relationship between body mass and body length). The observed inverse correlations between bR and bL are predicted by metabolic scaling theory that emphasizes resource/waste fluxes across external body surfaces, but contradict theory that emphasizes resource transport through internal networks. Geometric estimates of the scaling of surface area (SA) with body mass (bA) further show that ontogenetic shifts in bR and bA are positively correlated. These results support new metabolic scaling theory based on SA influences that may be applied to ontogenetic shifts in bR shown by many kinds of animals and plants. © 2015 The Author(s) Published by the Royal Society. All rights reserved.

Engineering strategy of yeast metabolism for higher alcohol production.

PubMed

Matsuda, Fumio; Furusawa, Chikara; Kondo, Takashi; Ishii, Jun; Shimizu, Hiroshi; Kondo, Akihiko

2011-09-08

While Saccharomyces cerevisiae is a promising host for cost-effective biorefinary processes due to its tolerance to various stresses during fermentation, the metabolically engineered S. cerevisiae strains exhibited rather limited production of higher alcohols than that of Escherichia coli. Since the structure of the central metabolism of S. cerevisiae is distinct from that of E. coli, there might be a problem in the structure of the central metabolism of S. cerevisiae. In this study, the potential production of higher alcohols by S. cerevisiae is compared to that of E. coli by employing metabolic simulation techniques. Based on the simulation results, novel metabolic engineering strategies for improving higher alcohol production by S. cerevisiae were investigated by in silico modifications of the metabolic models of S. cerevisiae. The metabolic simulations confirmed that the high production of butanols and propanols by the metabolically engineered E. coli strains is derived from the flexible behavior of their central metabolism. Reducing this flexibility by gene deletion is an effective strategy to restrict the metabolic states for producing target alcohols. In contrast, the lower yield using S. cerevisiae originates from the structurally limited flexibility of its central metabolism in which gene deletions severely reduced cell growth. The metabolic simulation demonstrated that the poor productivity of S. cerevisiae was improved by the introduction of E. coli genes to compensate the structural difference. This suggested that gene supplementation is a promising strategy for the metabolic engineering of S. cerevisiae to produce higher alcohols which should be the next challenge for the synthetic bioengineering of S. cerevisiae for the efficient production of higher alcohols.

Automation on the generation of genome-scale metabolic models.

PubMed

Reyes, R; Gamermann, D; Montagud, A; Fuente, D; Triana, J; Urchueguía, J F; de Córdoba, P Fernández

2012-12-01

Nowadays, the reconstruction of genome-scale metabolic models is a nonautomatized and interactive process based on decision making. This lengthy process usually requires a full year of one person's work in order to satisfactory collect, analyze, and validate the list of all metabolic reactions present in a specific organism. In order to write this list, one manually has to go through a huge amount of genomic, metabolomic, and physiological information. Currently, there is no optimal algorithm that allows one to automatically go through all this information and generate the models taking into account probabilistic criteria of unicity and completeness that a biologist would consider. This work presents the automation of a methodology for the reconstruction of genome-scale metabolic models for any organism. The methodology that follows is the automatized version of the steps implemented manually for the reconstruction of the genome-scale metabolic model of a photosynthetic organism, Synechocystis sp. PCC6803. The steps for the reconstruction are implemented in a computational platform (COPABI) that generates the models from the probabilistic algorithms that have been developed. For validation of the developed algorithm robustness, the metabolic models of several organisms generated by the platform have been studied together with published models that have been manually curated. Network properties of the models, like connectivity and average shortest mean path of the different models, have been compared and analyzed.

13C-Metabolic Flux Analysis: An Accurate Approach to Demystify Microbial Metabolism for Biochemical Production

PubMed Central

Guo, Weihua; Sheng, Jiayuan; Feng, Xueyang

2015-01-01

Metabolic engineering of various industrial microorganisms to produce chemicals, fuels, and drugs has raised interest since it is environmentally friendly, sustainable, and independent of nonrenewable resources. However, microbial metabolism is so complex that only a few metabolic engineering efforts have been able to achieve a satisfactory yield, titer or productivity of the target chemicals for industrial commercialization. In order to overcome this challenge, 13C Metabolic Flux Analysis (13C-MFA) has been continuously developed and widely applied to rigorously investigate cell metabolism and quantify the carbon flux distribution in central metabolic pathways. In the past decade, many 13C-MFA studies have been performed in academic labs and biotechnology industries to pinpoint key issues related to microbe-based chemical production. Insightful information about the metabolic rewiring has been provided to guide the development of the appropriate metabolic engineering strategies for improving the biochemical production. In this review, we will introduce the basics of 13C-MFA and illustrate how 13C-MFA has been applied via integration with metabolic engineering to identify and tackle the rate-limiting steps in biochemical production for various host microorganisms PMID:28952565

Metabolic impact of shivering during therapeutic temperature modulation: the Bedside Shivering Assessment Scale.

PubMed

Badjatia, Neeraj; Strongilis, Evangelia; Gordon, Errol; Prescutti, Mary; Fernandez, Luis; Fernandez, Andres; Buitrago, Manuel; Schmidt, J Michael; Ostapkovich, Noeleen D; Mayer, Stephan A

2008-12-01

Therapeutic temperature modulation is widely used in neurocritical care but commonly causes shivering, which can hamper the cooling process and result in increases in systemic metabolism. We sought to validate a grading scale to assist in the monitoring and control of shivering. A simple 4-point Bedside Shivering Assessment Scale was validated against continuous assessments of resting energy expenditure, oxygen consumption, and carbon dioxide production as measured by indirect calorimetry. Therapeutic temperature modulation for fever control or the induction of hypothermia was achieved with the use of a surface or endovascular device. Expected energy expenditure was calculated using the Harris-Benedict equation. A hypermetabolic index was calculated from the ratio of resting of energy expenditure to energy expenditure. Fifty consecutive cerebrovascular patients underwent indirect calorimetry between January 2006 and June 2007. Fifty-six percent were women, and mean age 63+/-16 years. The majority underwent fever control (n=40 [80%]) with a surface cooling device (n=44 [87%]) and had signs of shivering (Bedside Shivering Assessment Scale >0, 64% [n=34 of 50]). Low serum magnesium was independently associated with the presence of shivering (Bedside Shivering Assessment Scale >0; OR, 6.8; 95% CI, 1.7 to 28.0; P=0.01). The Bedside Shivering Assessment Scale was independently associated with the hypermetabolic index (W=16.3, P<0.001), oxygen consumption (W=26.3, P<0.001), resting energy expenditure (W=27.2, P<0.001), and carbon dioxide production (W=18.2, P<0.001) with a high level of interobserver reliability (kappa(w)=0.84, 95% CI, 0.81 to 0.86). The Bedside Shivering Assessment Scale is a simple and reliable tool for evaluating the metabolic stress of shivering.

Scaling of muscle metabolic enzymes: an historical perspective.

PubMed

Moyes, Christopher D; Genge, Christine E

2010-07-01

In this paper, we take an historical approach to reviewing research into the patterns of metabolic enzymes in muscle in relation to body size, focusing on mitochondrial enzymes. One of the first studies on allometric scaling of muscle enzymes was published in an early issue of this journal (George and Talesara, 1961 Comp. Biochem. Physiol. 3: 267-273). These researchers studied a number of locally available birds and a bat, measuring the activity of the mitochondrial enzyme succinate dehydrogenase in relation to body mass and muscle structure. Though the phenomenon of allometric scaling of metabolism was well recognized even 50 years earlier, this study was one of the first to explore the enzymatic underpinnings of the metabolic patterns in different animals. In this review, we begin by considering the George and Talesara study in the context of this early era in metabolic biochemistry and comparative physiology. We review subsequent studies in the last 50 years that continued the comparative analysis of enzyme patterns in relation to body size in diverse experimental models. This body of work identified a recurrent (though not ubiquitous) reciprocal relationship between oxidative and glycolytic enzymes. In the last 10 years, studies have focused on identifying the molecular mechanisms that determine the muscle metabolic enzyme phenotype. Copyright 2010 Elsevier Inc. All rights reserved.

Genome-scale metabolic model for Lactococcus lactis MG1363 and its application to the analysis of flavor formation.

PubMed

Flahaut, Nicolas A L; Wiersma, Anne; van de Bunt, Bert; Martens, Dirk E; Schaap, Peter J; Sijtsma, Lolke; Dos Santos, Vitor A Martins; de Vos, Willem M

2013-10-01

Lactococcus lactis subsp. cremoris MG1363 is a paradigm strain for lactococci used in industrial dairy fermentations. However, despite of its importance for process development, no genome-scale metabolic model has been reported thus far. Moreover, current models for other lactococci only focus on growth and sugar degradation. A metabolic model that includes nitrogen metabolism and flavor-forming pathways is instrumental for the understanding and designing new industrial applications of these lactic acid bacteria. A genome-scale, constraint-based model of the metabolism and transport in L. lactis MG1363, accounting for 518 genes, 754 reactions, and 650 metabolites, was developed and experimentally validated. Fifty-nine reactions are directly or indirectly involved in flavor formation. Flux Balance Analysis and Flux Variability Analysis were used to investigate flux distributions within the whole metabolic network. Anaerobic carbon-limited continuous cultures were used for estimating the energetic parameters. A thorough model-driven analysis showing a highly flexible nitrogen metabolism, e.g., branched-chain amino acid catabolism which coupled with the redox balance, is pivotal for the prediction of the formation of different flavor compounds. Furthermore, the model predicted the formation of volatile sulfur compounds as a result of the fermentation. These products were subsequently identified in the experimental fermentations carried out. Thus, the genome-scale metabolic model couples the carbon and nitrogen metabolism in L. lactis MG1363 with complete known catabolic pathways leading to flavor formation. The model provided valuable insights into the metabolic networks underlying flavor formation and has the potential to contribute to new developments in dairy industries and cheese-flavor research.

Genome-scale model guided design of Propionibacterium for enhanced propionic acid production.

PubMed

Navone, Laura; McCubbin, Tim; Gonzalez-Garcia, Ricardo A; Nielsen, Lars K; Marcellin, Esteban

2018-06-01

Production of propionic acid by fermentation of propionibacteria has gained increasing attention in the past few years. However, biomanufacturing of propionic acid cannot compete with the current oxo-petrochemical synthesis process due to its well-established infrastructure, low oil prices and the high downstream purification costs of microbial production. Strain improvement to increase propionic acid yield is the best alternative to reduce downstream purification costs. The recent generation of genome-scale models for a number of Propionibacterium species facilitates the rational design of metabolic engineering strategies and provides a new opportunity to explore the metabolic potential of the Wood-Werkman cycle. Previous strategies for strain improvement have individually targeted acid tolerance, rate of propionate production or minimisation of by-products. Here we used the P. freudenreichii subsp . shermanii and the pan- Propionibacterium genome-scale metabolic models (GEMs) to simultaneously target these combined issues. This was achieved by focussing on strategies which yield higher energies and directly suppress acetate formation. Using P. freudenreichii subsp . shermanii , two strategies were assessed. The first tested the ability to manipulate the redox balance to favour propionate production by over-expressing the first two enzymes of the pentose-phosphate pathway (PPP), Zwf (glucose-6-phosphate 1-dehydrogenase) and Pgl (6-phosphogluconolactonase). Results showed a 4-fold increase in propionate to acetate ratio during the exponential growth phase. Secondly, the ability to enhance the energy yield from propionate production by over-expressing an ATP-dependent phosphoenolpyruvate carboxykinase (PEPCK) and sodium-pumping methylmalonyl-CoA decarboxylase (MMD) was tested, which extended the exponential growth phase. Together, these strategies demonstrate that in silico design strategies are predictive and can be used to reduce by-product formation in

Phosphoenolpyruvate Transporter Enables Targeted Perturbation During Metabolic Analysis of L-Phenylalanine Production With Escherichia coli.

PubMed

Tröndle, Julia; Albermann, Christoph; Weiner, Michael; Sprenger, Georg A; Weuster-Botz, Dirk

2018-05-01

Usually perturbation of the metabolism of cells by addition of substrates is applied for metabolic analysis of production organisms, but perturbation studies are restricted to the endogenous substrates of the cells under study. The goal of this study is to overcome this limitation by making phosphoenolpyruvate (PEP) available for perturbation studies with Escherichia coli producing L-phenylalanine. A production strain overexpressing a PEP-transporter variant (UhpT-D388C) is applied in a standardized fed-batch production-process on a 42 L-scale. Four parallel short-term perturbation experiments of 20 min are performed with glucose and glycerol as fed-batch carbon sources after rapid media transition of cells from the production-process. PEP is added after 9 min and is immediately consumed by the cells with up to 1.5 mmol g CDW -1  h -1 . L-phenylalanine production rates increased by up to 200% after addition of PEP. This clearly indicates an intracellular PEP-limitation in the L-phenylalanine production strain under study. Thus, it is shown that overexpressing specific transporters for analytical reasons makes exogenous substrates available as perturbation substrates for metabolic analyses of cells sampled from production-processes and thereby allows a very targeted perturbation of whole-cell metabolism. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

A genome-scale metabolic reconstruction of Pseudomonas putida KT2440: iJN746 as a cell factory.

PubMed

Nogales, Juan; Palsson, Bernhard Ø; Thiele, Ines

2008-09-16

Pseudomonas putida is the best studied pollutant degradative bacteria and is harnessed by industrial biotechnology to synthesize fine chemicals. Since the publication of P. putida KT2440's genome, some in silico analyses of its metabolic and biotechnology capacities have been published. However, global understanding of the capabilities of P. putida KT2440 requires the construction of a metabolic model that enables the integration of classical experimental data along with genomic and high-throughput data. The constraint-based reconstruction and analysis (COBRA) approach has been successfully used to build and analyze in silico genome-scale metabolic reconstructions. We present a genome-scale reconstruction of P. putida KT2440's metabolism, iJN746, which was constructed based on genomic, biochemical, and physiological information. This manually-curated reconstruction accounts for 746 genes, 950 reactions, and 911 metabolites. iJN746 captures biotechnologically relevant pathways, including polyhydroxyalkanoate synthesis and catabolic pathways of aromatic compounds (e.g., toluene, benzoate, phenylacetate, nicotinate), not described in other metabolic reconstructions or biochemical databases. The predictive potential of iJN746 was validated using experimental data including growth performance and gene deletion studies. Furthermore, in silico growth on toluene was found to be oxygen-limited, suggesting the existence of oxygen-efficient pathways not yet annotated in P. putida's genome. Moreover, we evaluated the production efficiency of polyhydroxyalkanoates from various carbon sources and found fatty acids as the most prominent candidates, as expected. Here we presented the first genome-scale reconstruction of P. putida, a biotechnologically interesting all-surrounder. Taken together, this work illustrates the utility of iJN746 as i) a knowledge-base, ii) a discovery tool, and iii) an engineering platform to explore P. putida's potential in bioremediation and bioplastic

Genome scale metabolic reconstruction of Chlorella variabilis for exploring its metabolic potential for biofuels.

PubMed

Juneja, Ankita; Chaplen, Frank W R; Murthy, Ganti S

2016-08-01

A compartmentalized genome scale metabolic network was reconstructed for Chlorella variabilis to offer insight into various metabolic potentials from this alga. The model, iAJ526, was reconstructed with 1455 reactions, 1236 metabolites and 526 genes. 21% of the reactions were transport reactions and about 81% of the total reactions were associated with enzymes. Along with gap filling reactions, 2 major sub-pathways were added to the model, chitosan synthesis and rhamnose metabolism. The reconstructed model had reaction participation of 4.3 metabolites per reaction and average lethality fraction of 0.21. The model was effective in capturing the growth of C. variabilis under three light conditions (white, red and red+blue light) with fair agreement. This reconstructed metabolic network will serve an important role in systems biology for further exploration of metabolism for specific target metabolites and enable improved characteristics in the strain through metabolic engineering. Copyright © 2016 Elsevier Ltd. All rights reserved.

Differentiating causality and correlation in allometric scaling: ant colony size drives metabolic hypometry

PubMed Central

Ochs, Alison; Fewell, Jennifer H.; Harrison, Jon F.

2017-01-01

Metabolic rates of individual animals and social insect colonies generally scale hypometrically, with mass-specific metabolic rates decreasing with increasing size. Although this allometry has wide ranging effects on social behaviour, ecology and evolution, its causes remain controversial. Because it is difficult to experimentally manipulate body size of organisms, most studies of metabolic scaling depend on correlative data, limiting their ability to determine causation. To overcome this limitation, we experimentally reduced the size of harvester ant colonies (Pogonomyrmex californicus) and quantified the consequent increase in mass-specific metabolic rates. Our results clearly demonstrate a causal relationship between colony size and hypometric changes in metabolic rate that could not be explained by changes in physical density. These findings provide evidence against prominent models arguing that the hypometric scaling of metabolic rate is primarily driven by constraints on resource delivery or surface area/volume ratios, because colonies were provided with excess food and colony size does not affect individual oxygen or nutrient transport. We found that larger colonies had lower median walking speeds and relatively more stationary ants and including walking speed as a variable in the mass-scaling allometry greatly reduced the amount of residual variation in the model, reinforcing the role of behaviour in metabolic allometry. Following the experimental size reduction, however, the proportion of stationary ants increased, demonstrating that variation in locomotory activity cannot solely explain hypometric scaling of metabolic rates in these colonies. Based on prior studies of this species, the increase in metabolic rate in size-reduced colonies could be due to increased anabolic processes associated with brood care and colony growth. PMID:28228514

The Genome-Based Metabolic Systems Engineering to Boost Levan Production in a Halophilic Bacterial Model.

PubMed

Aydin, Busra; Ozer, Tugba; Oner, Ebru Toksoy; Arga, Kazim Yalcin

2018-03-01

Metabolic systems engineering is being used to redirect microbial metabolism for the overproduction of chemicals of interest with the aim of transforming microbial hosts into cellular factories. In this study, a genome-based metabolic systems engineering approach was designed and performed to improve biopolymer biosynthesis capability of a moderately halophilic bacterium Halomonas smyrnensis AAD6 T producing levan, which is a fructose homopolymer with many potential uses in various industries and medicine. For this purpose, the genome-scale metabolic model for AAD6 T was used to characterize the metabolic resource allocation, specifically to design metabolic engineering strategies for engineered bacteria with enhanced levan production capability. Simulations were performed in silico to determine optimal gene knockout strategies to develop new strains with enhanced levan production capability. The majority of the gene knockout strategies emphasized the vital role of the fructose uptake mechanism, and pointed out the fructose-specific phosphotransferase system (PTS fru ) as the most promising target for further metabolic engineering studies. Therefore, the PTS fru of AAD6 T was restructured with insertional mutagenesis and triparental mating techniques to construct a novel, engineered H. smyrnensis strain, BMA14. Fermentation experiments were carried out to demonstrate the high efficiency of the mutant strain BMA14 in terms of final levan concentration, sucrose consumption rate, and sucrose conversion efficiency, when compared to the AAD6 T . The genome-based metabolic systems engineering approach presented in this study might be considered an efficient framework to redirect microbial metabolism for the overproduction of chemicals of interest, and the novel strain BMA14 might be considered a potential microbial cell factory for further studies aimed to design levan production processes with lower production costs.

Do Performance-Safety Tradeoffs Cause Hypometric Metabolic Scaling in Animals?

PubMed

Harrison, Jon F

2017-09-01

Hypometric scaling of aerobic metabolism in animals has been widely attributed to constraints on oxygen (O 2 ) supply in larger animals, but recent findings demonstrate that O 2 supply balances with need regardless of size. Larger animals also do not exhibit evidence of compensation for O 2 supply limitation. Because declining metabolic rates (MRs) are tightly linked to fitness, this provides significant evidence against the hypothesis that constraints on supply drive hypometric scaling. As an alternative, ATP demand might decline in larger animals because of performance-safety tradeoffs. Larger animals, which typically reproduce later, exhibit risk-reducing strategies that lower MR. Conversely, smaller animals are more strongly selected for growth and costly neurolocomotory performance, elevating metabolism. Copyright © 2017 Elsevier Ltd. All rights reserved.

Genome-Scale Metabolic Modeling of Archaea Lends Insight into Diversity of Metabolic Function

PubMed Central

2017-01-01

Decades of biochemical, bioinformatic, and sequencing data are currently being systematically compiled into genome-scale metabolic reconstructions (GEMs). Such reconstructions are knowledge-bases useful for engineering, modeling, and comparative analysis. Here we review the fifteen GEMs of archaeal species that have been constructed to date. They represent primarily members of the Euryarchaeota with three-quarters comprising representative of methanogens. Unlike other reviews on GEMs, we specially focus on archaea. We briefly review the GEM construction process and the genealogy of the archaeal models. The major insights gained during the construction of these models are then reviewed with specific focus on novel metabolic pathway predictions and growth characteristics. Metabolic pathway usage is discussed in the context of the composition of each organism's biomass and their specific energy and growth requirements. We show how the metabolic models can be used to study the evolution of metabolism in archaea. Conservation of particular metabolic pathways can be studied by comparing reactions using the genes associated with their enzymes. This demonstrates the utility of GEMs to evolutionary studies, far beyond their original purpose of metabolic modeling; however, much needs to be done before archaeal models are as extensively complete as those for bacteria. PMID:28133437

Hypothalamic Leucine Metabolism Regulates Liver Glucose Production

PubMed Central

Su, Ya; Lam, Tony K.T.; He, Wu; Pocai, Alessandro; Bryan, Joseph; Aguilar-Bryan, Lydia; Gutiérrez-Juárez, Roger

2012-01-01

Amino acids profoundly affect insulin action and glucose metabolism in mammals. Here, we investigated the role of the mediobasal hypothalamus (MBH), a key center involved in nutrient-dependent metabolic regulation. Specifically, we tested the novel hypothesis that the metabolism of leucine within the MBH couples the central sensing of leucine with the control of glucose production by the liver. We performed either central (MBH) or systemic infusions of leucine in Sprague-Dawley male rats during basal pancreatic insulin clamps in combination with various pharmacological and molecular interventions designed to modulate leucine metabolism in the MBH. We also examined the role of hypothalamic ATP-sensitive K+ channels (KATP channels) in the effects of leucine. Enhancing the metabolism of leucine acutely in the MBH lowered blood glucose through a biochemical network that was insensitive to rapamycin but strictly dependent on the hypothalamic metabolism of leucine to α-ketoisocaproic acid and, further, insensitive to acetyl- and malonyl-CoA. Functional KATP channels were also required. Importantly, molecular attenuation of this central sensing mechanism in rats conferred susceptibility to developing hyperglycemia. We postulate that the metabolic sensing of leucine in the MBH is a previously unrecognized mechanism for the regulation of hepatic glucose production required to maintain glucose homeostasis. PMID:22187376

Metabolic networks evolve towards states of maximum entropy production.

PubMed

Unrean, Pornkamol; Srienc, Friedrich

2011-11-01

A metabolic network can be described by a set of elementary modes or pathways representing discrete metabolic states that support cell function. We have recently shown that in the most likely metabolic state the usage probability of individual elementary modes is distributed according to the Boltzmann distribution law while complying with the principle of maximum entropy production. To demonstrate that a metabolic network evolves towards such state we have carried out adaptive evolution experiments with Thermoanaerobacterium saccharolyticum operating with a reduced metabolic functionality based on a reduced set of elementary modes. In such reduced metabolic network metabolic fluxes can be conveniently computed from the measured metabolite secretion pattern. Over a time span of 300 generations the specific growth rate of the strain continuously increased together with a continuous increase in the rate of entropy production. We show that the rate of entropy production asymptotically approaches the maximum entropy production rate predicted from the state when the usage probability of individual elementary modes is distributed according to the Boltzmann distribution. Therefore, the outcome of evolution of a complex biological system can be predicted in highly quantitative terms using basic statistical mechanical principles. Copyright © 2011 Elsevier Inc. All rights reserved.

Metabolic network reconstruction and genome-scale model of butanol-producing strain Clostridium beijerinckii NCIMB 8052

PubMed Central

2011-01-01

Background Solventogenic clostridia offer a sustainable alternative to petroleum-based production of butanol--an important chemical feedstock and potential fuel additive or replacement. C. beijerinckii is an attractive microorganism for strain design to improve butanol production because it (i) naturally produces the highest recorded butanol concentrations as a byproduct of fermentation; and (ii) can co-ferment pentose and hexose sugars (the primary products from lignocellulosic hydrolysis). Interrogating C. beijerinckii metabolism from a systems viewpoint using constraint-based modeling allows for simulation of the global effect of genetic modifications. Results We present the first genome-scale metabolic model (iCM925) for C. beijerinckii, containing 925 genes, 938 reactions, and 881 metabolites. To build the model we employed a semi-automated procedure that integrated genome annotation information from KEGG, BioCyc, and The SEED, and utilized computational algorithms with manual curation to improve model completeness. Interestingly, we found only a 34% overlap in reactions collected from the three databases--highlighting the importance of evaluating the predictive accuracy of the resulting genome-scale model. To validate iCM925, we conducted fermentation experiments using the NCIMB 8052 strain, and evaluated the ability of the model to simulate measured substrate uptake and product production rates. Experimentally observed fermentation profiles were found to lie within the solution space of the model; however, under an optimal growth objective, additional constraints were needed to reproduce the observed profiles--suggesting the existence of selective pressures other than optimal growth. Notably, a significantly enriched fraction of actively utilized reactions in simulations--constrained to reflect experimental rates--originated from the set of reactions that overlapped between all three databases (P = 3.52 × 10-9, Fisher's exact test). Inhibition of the

A genome-scale metabolic network reconstruction of tomato (Solanum lycopersicum L.) and its application to photorespiratory metabolism.

PubMed

Yuan, Huili; Cheung, C Y Maurice; Poolman, Mark G; Hilbers, Peter A J; van Riel, Natal A W

2016-01-01

Tomato (Solanum lycopersicum L.) has been studied extensively due to its high economic value in the market, and high content in health-promoting antioxidant compounds. Tomato is also considered as an excellent model organism for studying the development and metabolism of fleshy fruits. However, the growth, yield and fruit quality of tomatoes can be affected by drought stress, a common abiotic stress for tomato. To investigate the potential metabolic response of tomato plants to drought, we reconstructed iHY3410, a genome-scale metabolic model of tomato leaf, and used this metabolic network to simulate tomato leaf metabolism. The resulting model includes 3410 genes and 2143 biochemical and transport reactions distributed across five intracellular organelles including cytosol, plastid, mitochondrion, peroxisome and vacuole. The model successfully described the known metabolic behaviour of tomato leaf under heterotrophic and phototrophic conditions. The in silico investigation of the metabolic characteristics for photorespiration and other relevant metabolic processes under drought stress suggested that: (i) the flux distributions through the mevalonate (MVA) pathway under drought were distinct from that under normal conditions; and (ii) the changes in fluxes through core metabolic pathways with varying flux ratio of RubisCO carboxylase to oxygenase may contribute to the adaptive stress response of plants. In addition, we improved on previous studies of reaction essentiality analysis for leaf metabolism by including potential alternative routes for compensating reaction knockouts. Altogether, the genome-scale model provides a sound framework for investigating tomato metabolism and gives valuable insights into the functional consequences of abiotic stresses. © 2015 The Authors.The Plant Journal published by Society for Experimental Biology and John Wiley & Sons Ltd.

Find_tfSBP: find thermodynamics-feasible and smallest balanced pathways with high yield from large-scale metabolic networks.

PubMed

Xu, Zixiang; Sun, Jibin; Wu, Qiaqing; Zhu, Dunming

2017-12-11

Biologically meaningful metabolic pathways are important references in the design of industrial bacterium. At present, constraint-based method is the only way to model and simulate a genome-scale metabolic network under steady-state criteria. Due to the inadequate assumption of the relationship in gene-enzyme-reaction as one-to-one unique association, computational difficulty or ignoring the yield from substrate to product, previous pathway finding approaches can't be effectively applied to find out the high yield pathways that are mass balanced in stoichiometry. In addition, the shortest pathways may not be the pathways with high yield. At the same time, a pathway, which exists in stoichiometry, may not be feasible in thermodynamics. By using mixed integer programming strategy, we put forward an algorithm to identify all the smallest balanced pathways which convert the source compound to the target compound in large-scale metabolic networks. The resulting pathways by our method can finely satisfy the stoichiometric constraints and non-decomposability condition. Especially, the functions of high yield and thermodynamics feasibility have been considered in our approach. This tool is tailored to direct the metabolic engineering practice to enlarge the metabolic potentials of industrial strains by integrating the extensive metabolic network information built from systems biology dataset.

Analysis of metabolic networks of Streptomyces leeuwenhoekii C34 by means of a genome scale model: Prediction of modifications that enhance the production of specialized metabolites.

PubMed

Razmilic, Valeria; Castro, Jean F; Andrews, Barbara; Asenjo, Juan A

2018-07-01

The first genome scale model (GSM) for Streptomyces leeuwenhoekii C34 was developed to study the biosynthesis pathways of specialized metabolites and to find metabolic engineering targets for enhancing their production. The model, iVR1007, consists of 1,722 reactions, 1,463 metabolites, and 1,007 genes, it includes the biosynthesis pathways of chaxamycins, chaxalactins, desferrioxamines, ectoine, and other specialized metabolites. iVR1007 was validated using experimental information of growth on 166 different sources of carbon, nitrogen and phosphorous, showing an 83.7% accuracy. The model was used to predict metabolic engineering targets for enhancing the biosynthesis of chaxamycins and chaxalactins. Gene knockouts, such as sle03600 (L-homoserine O-acetyltransferase), and sle39090 (trehalose-phosphate synthase), that enhance the production of the specialized metabolites by increasing the pool of precursors were identified. Using the algorithm of flux scanning based on enforced objective flux (FSEOF) implemented in python, 35 and 25 over-expression targets for increasing the production of chaxamycin A and chaxalactin A, respectively, that were not directly associated with their biosynthesis routes were identified. Nineteen over-expression targets that were common to the two specialized metabolites studied, like the over-expression of the acetyl carboxylase complex (sle47660 (accA) and any of the following genes: sle44630 (accA_1) or sle39830 (accA_2) or sle27560 (bccA) or sle59710) were identified. The predicted knockouts and over-expression targets will be used to perform metabolic engineering of S. leeuwenhoekii C34 and obtain overproducer strains. © 2018 Wiley Periodicals, Inc.

MOST-visualization: software for producing automated textbook-style maps of genome-scale metabolic networks.

PubMed

Kelley, James J; Maor, Shay; Kim, Min Kyung; Lane, Anatoliy; Lun, Desmond S

2017-08-15

Visualization of metabolites, reactions and pathways in genome-scale metabolic networks (GEMs) can assist in understanding cellular metabolism. Three attributes are desirable in software used for visualizing GEMs: (i) automation, since GEMs can be quite large; (ii) production of understandable maps that provide ease in identification of pathways, reactions and metabolites; and (iii) visualization of the entire network to show how pathways are interconnected. No software currently exists for visualizing GEMs that satisfies all three characteristics, but MOST-Visualization, an extension of the software package MOST (Metabolic Optimization and Simulation Tool), satisfies (i), and by using a pre-drawn overview map of metabolism based on the Roche map satisfies (ii) and comes close to satisfying (iii). MOST is distributed for free on the GNU General Public License. The software and full documentation are available at http://most.ccib.rutgers.edu/. dslun@rutgers.edu. Supplementary data are available at Bioinformatics online. © The Author (2017). Published by Oxford University Press. All rights reserved. For Permissions, please email: journals.permissions@oup.com

Intraspecific Scaling of the Resting and Maximum Metabolic Rates of the Crucian Carp (Carassius auratus)

PubMed Central

Huang, Qingda; Zhang, Yurong; Liu, Shuting; Wang, Wen; Luo, Yiping

2013-01-01

The question of how the scaling of metabolic rate with body mass (M) is achieved in animals is unresolved. Here, we tested the cell metabolism hypothesis and the organ size hypothesis by assessing the mass scaling of the resting metabolic rate (RMR), maximum metabolic rate (MMR), erythrocyte size, and the masses of metabolically active organs in the crucian carp (Carassius auratus). The M of the crucian carp ranged from 4.5 to 323.9 g, representing an approximately 72-fold difference. The RMR and MMR increased with M according to the allometric equations RMR = 0.212M 0.776 and MMR = 0.753M 0.785. The scaling exponents for RMR (b r) and MMR (b m) obtained in crucian carp were close to each other. Thus, the factorial aerobic scope remained almost constant with increasing M. Although erythrocyte size was negatively correlated with both mass-specific RMR and absolute RMR adjusted to M, it and all other hematological parameters showed no significant relationship with M. These data demonstrate that the cell metabolism hypothesis does not describe metabolic scaling in the crucian carp, suggesting that erythrocyte size may not represent the general size of other cell types in this fish and the metabolic activity of cells may decrease as fish grows. The mass scaling exponents of active organs was lower than 1 while that of inactive organs was greater than 1, which suggests that the mass scaling of the RMR can be partly due to variance in the proportion of active/inactive organs in crucian carp. Furthermore, our results provide additional evidence supporting the correlation between locomotor capacity and metabolic scaling. PMID:24376588

BiGG: a Biochemical Genetic and Genomic knowledgebase of large scale metabolic reconstructions

PubMed Central

2010-01-01

Background Genome-scale metabolic reconstructions under the Constraint Based Reconstruction and Analysis (COBRA) framework are valuable tools for analyzing the metabolic capabilities of organisms and interpreting experimental data. As the number of such reconstructions and analysis methods increases, there is a greater need for data uniformity and ease of distribution and use. Description We describe BiGG, a knowledgebase of Biochemically, Genetically and Genomically structured genome-scale metabolic network reconstructions. BiGG integrates several published genome-scale metabolic networks into one resource with standard nomenclature which allows components to be compared across different organisms. BiGG can be used to browse model content, visualize metabolic pathway maps, and export SBML files of the models for further analysis by external software packages. Users may follow links from BiGG to several external databases to obtain additional information on genes, proteins, reactions, metabolites and citations of interest. Conclusions BiGG addresses a need in the systems biology community to have access to high quality curated metabolic models and reconstructions. It is freely available for academic use at http://bigg.ucsd.edu. PMID:20426874

A Protocol for Generating and Exchanging (Genome-Scale) Metabolic Resource Allocation Models.

PubMed

Reimers, Alexandra-M; Lindhorst, Henning; Waldherr, Steffen

2017-09-06

In this article, we present a protocol for generating a complete (genome-scale) metabolic resource allocation model, as well as a proposal for how to represent such models in the systems biology markup language (SBML). Such models are used to investigate enzyme levels and achievable growth rates in large-scale metabolic networks. Although the idea of metabolic resource allocation studies has been present in the field of systems biology for some years, no guidelines for generating such a model have been published up to now. This paper presents step-by-step instructions for building a (dynamic) resource allocation model, starting with prerequisites such as a genome-scale metabolic reconstruction, through building protein and noncatalytic biomass synthesis reactions and assigning turnover rates for each reaction. In addition, we explain how one can use SBML level 3 in combination with the flux balance constraints and our resource allocation modeling annotation to represent such models.

Production of L-valine from metabolically engineered Corynebacterium glutamicum.

PubMed

Wang, Xiaoyuan; Zhang, Hailing; Quinn, Peter J

2018-05-01

L-Valine is one of the three branched-chain amino acids (valine, leucine, and isoleucine) essential for animal health and important in metabolism; therefore, it is widely added in the products of food, medicine, and feed. L-Valine is predominantly produced through microbial fermentation, and the production efficiency largely depends on the quality of microorganisms. In recent years, continuing efforts have been made in revealing the mechanisms and regulation of L-valine biosynthesis in Corynebacterium glutamicum, the most utilitarian bacterium for amino acid production. Metabolic engineering based on the metabolic biosynthesis and regulation of L-valine provides an effective alternative to the traditional breeding for strain development. Industrially competitive L-valine-producing C. glutamicum strains have been constructed by genetically defined metabolic engineering. This article reviews the global metabolic and regulatory networks responsible for L-valine biosynthesis, the molecular mechanisms of regulation, and the strategies employed in C. glutamicum strain engineering.

Supervised de novo reconstruction of metabolic pathways from metabolome-scale compound sets

PubMed Central

Kotera, Masaaki; Tabei, Yasuo; Yamanishi, Yoshihiro; Tokimatsu, Toshiaki; Goto, Susumu

2013-01-01

Motivation: The metabolic pathway is an important biochemical reaction network involving enzymatic reactions among chemical compounds. However, it is assumed that a large number of metabolic pathways remain unknown, and many reactions are still missing even in known pathways. Therefore, the most important challenge in metabolomics is the automated de novo reconstruction of metabolic pathways, which includes the elucidation of previously unknown reactions to bridge the metabolic gaps. Results: In this article, we develop a novel method to reconstruct metabolic pathways from a large compound set in the reaction-filling framework. We define feature vectors representing the chemical transformation patterns of compound–compound pairs in enzymatic reactions using chemical fingerprints. We apply a sparsity-induced classifier to learn what we refer to as ‘enzymatic-reaction likeness’, i.e. whether compound pairs are possibly converted to each other by enzymatic reactions. The originality of our method lies in the search for potential reactions among many compounds at a time, in the extraction of reaction-related chemical transformation patterns and in the large-scale applicability owing to the computational efficiency. In the results, we demonstrate the usefulness of our proposed method on the de novo reconstruction of 134 metabolic pathways in Kyoto Encyclopedia of Genes and Genomes (KEGG). Our comprehensively predicted reaction networks of 15 698 compounds enable us to suggest many potential pathways and to increase research productivity in metabolomics. Availability: Softwares are available on request. Supplementary material are available at http://web.kuicr.kyoto-u.ac.jp/supp/kot/ismb2013/. Contact: goto@kuicr.kyoto-u.ac.jp PMID:23812977

A general model for metabolic scaling in self-similar asymmetric networks

PubMed Central

Savage, Van M.; Enquist, Brian J.

2017-01-01

How a particular attribute of an organism changes or scales with its body size is known as an allometry. Biological allometries, such as metabolic scaling, have been hypothesized to result from selection to maximize how vascular networks fill space yet minimize internal transport distances and resistances. The West, Brown, Enquist (WBE) model argues that these two principles (space-filling and energy minimization) are (i) general principles underlying the evolution of the diversity of biological networks across plants and animals and (ii) can be used to predict how the resulting geometry of biological networks then governs their allometric scaling. Perhaps the most central biological allometry is how metabolic rate scales with body size. A core assumption of the WBE model is that networks are symmetric with respect to their geometric properties. That is, any two given branches within the same generation in the network are assumed to have identical lengths and radii. However, biological networks are rarely if ever symmetric. An open question is: Does incorporating asymmetric branching change or influence the predictions of the WBE model? We derive a general network model that relaxes the symmetric assumption and define two classes of asymmetrically bifurcating networks. We show that asymmetric branching can be incorporated into the WBE model. This asymmetric version of the WBE model results in several theoretical predictions for the structure, physiology, and metabolism of organisms, specifically in the case for the cardiovascular system. We show how network asymmetry can now be incorporated in the many allometric scaling relationships via total network volume. Most importantly, we show that the 3/4 metabolic scaling exponent from Kleiber’s Law can still be attained within many asymmetric networks. PMID:28319153

A general model for metabolic scaling in self-similar asymmetric networks.

PubMed

Brummer, Alexander Byers; Savage, Van M; Enquist, Brian J

2017-03-01

How a particular attribute of an organism changes or scales with its body size is known as an allometry. Biological allometries, such as metabolic scaling, have been hypothesized to result from selection to maximize how vascular networks fill space yet minimize internal transport distances and resistances. The West, Brown, Enquist (WBE) model argues that these two principles (space-filling and energy minimization) are (i) general principles underlying the evolution of the diversity of biological networks across plants and animals and (ii) can be used to predict how the resulting geometry of biological networks then governs their allometric scaling. Perhaps the most central biological allometry is how metabolic rate scales with body size. A core assumption of the WBE model is that networks are symmetric with respect to their geometric properties. That is, any two given branches within the same generation in the network are assumed to have identical lengths and radii. However, biological networks are rarely if ever symmetric. An open question is: Does incorporating asymmetric branching change or influence the predictions of the WBE model? We derive a general network model that relaxes the symmetric assumption and define two classes of asymmetrically bifurcating networks. We show that asymmetric branching can be incorporated into the WBE model. This asymmetric version of the WBE model results in several theoretical predictions for the structure, physiology, and metabolism of organisms, specifically in the case for the cardiovascular system. We show how network asymmetry can now be incorporated in the many allometric scaling relationships via total network volume. Most importantly, we show that the 3/4 metabolic scaling exponent from Kleiber's Law can still be attained within many asymmetric networks.

Metabolic Design and Control for Production in Prokaryotes

DOE Office of Scientific and Technical Information (OSTI.GOV)

Chhabra, Swapnil R.; Keasling, J.D.

2010-11-10

Prokaryotic life on earth is manifested by its diversity and omnipresence. These microbes serve as natural sources of a large variety of compounds with the potential to serve the ever growing, medicinal, chemical and transportation needs of the human population. However, commercially viable production of these compounds can be realized only through significant improvement of the native production capacity of natural isolates. The most favorable way to achieve this goal is through the genetic manipulation of metabolic pathways that direct the production of these molecules. While random mutagenesis and screening have dominated the industrial production of such compounds in themore » past our increased understanding of microbial physiology over the last five decades has shifted this trend towards rational approaches for metabolic design. Major drivers of this trend include recombinant DNA technology, high throughput characterization of macromolecular cellular components, quantitative modeling for metabolic engine ring, targeted combinatorial engineering and synthetic biology. In this chapter we track the evolution of microbial engineering technologies from the black box era of random mutagenesis to the science and engineering-driven era of metabolic design.« less

Genome-Scale Metabolic Model for the Green Alga Chlorella vulgaris UTEX 395 Accurately Predicts Phenotypes under Autotrophic, Heterotrophic, and Mixotrophic Growth Conditions1

PubMed Central

Zuñiga, Cristal; Li, Chien-Ting; Zielinski, Daniel C.; Guarnieri, Michael T.; Antoniewicz, Maciek R.; Zengler, Karsten

2016-01-01

The green microalga Chlorella vulgaris has been widely recognized as a promising candidate for biofuel production due to its ability to store high lipid content and its natural metabolic versatility. Compartmentalized genome-scale metabolic models constructed from genome sequences enable quantitative insight into the transport and metabolism of compounds within a target organism. These metabolic models have long been utilized to generate optimized design strategies for an improved production process. Here, we describe the reconstruction, validation, and application of a genome-scale metabolic model for C. vulgaris UTEX 395, iCZ843. The reconstruction represents the most comprehensive model for any eukaryotic photosynthetic organism to date, based on the genome size and number of genes in the reconstruction. The highly curated model accurately predicts phenotypes under photoautotrophic, heterotrophic, and mixotrophic conditions. The model was validated against experimental data and lays the foundation for model-driven strain design and medium alteration to improve yield. Calculated flux distributions under different trophic conditions show that a number of key pathways are affected by nitrogen starvation conditions, including central carbon metabolism and amino acid, nucleotide, and pigment biosynthetic pathways. Furthermore, model prediction of growth rates under various medium compositions and subsequent experimental validation showed an increased growth rate with the addition of tryptophan and methionine. PMID:27372244

Kinetic modeling of cell metabolism for microbial production.

PubMed

Costa, Rafael S; Hartmann, Andras; Vinga, Susana

2016-02-10

Kinetic models of cellular metabolism are important tools for the rational design of metabolic engineering strategies and to explain properties of complex biological systems. The recent developments in high-throughput experimental data are leading to new computational approaches for building kinetic models of metabolism. Herein, we briefly survey the available databases, standards and software tools that can be applied for kinetic models of metabolism. In addition, we give an overview about recently developed ordinary differential equations (ODE)-based kinetic models of metabolism and some of the main applications of such models are illustrated in guiding metabolic engineering design. Finally, we review the kinetic modeling approaches of large-scale networks that are emerging, discussing their main advantages, challenges and limitations. Copyright © 2015 Elsevier B.V. All rights reserved.

Computational modelling of genome-scale metabolic networks and its application to CHO cell cultures.

PubMed

Rejc, Živa; Magdevska, Lidija; Tršelič, Tilen; Osolin, Timotej; Vodopivec, Rok; Mraz, Jakob; Pavliha, Eva; Zimic, Nikolaj; Cvitanović, Tanja; Rozman, Damjana; Moškon, Miha; Mraz, Miha

2017-09-01

Genome-scale metabolic models (GEMs) have become increasingly important in recent years. Currently, GEMs are the most accurate in silico representation of the genotype-phenotype link. They allow us to study complex networks from the systems perspective. Their application may drastically reduce the amount of experimental and clinical work, improve diagnostic tools and increase our understanding of complex biological phenomena. GEMs have also demonstrated high potential for the optimisation of bio-based production of recombinant proteins. Herein, we review the basic concepts, methods, resources and software tools used for the reconstruction and application of GEMs. We overview the evolution of the modelling efforts devoted to the metabolism of Chinese Hamster Ovary (CHO) cells. We present a case study on CHO cell metabolism under different amino acid depletions. This leads us to the identification of the most influential as well as essential amino acids in selected CHO cell lines. Copyright © 2017 Elsevier Ltd. All rights reserved.

Toward systems metabolic engineering of Aspergillus and Pichia species for the production of chemicals and biofuels.

PubMed

Caspeta, Luis; Nielsen, Jens

2013-05-01

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. Copyright © 2013 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Biome-specific scaling of ocean productivity, temperature, and carbon export efficiency

NASA Astrophysics Data System (ADS)

Britten, Gregory L.; Primeau, François W.

2016-05-01

Mass conservation and metabolic theory place constraints on how marine export production (EP) scales with net primary productivity (NPP) and sea surface temperature (SST); however, little is empirically known about how these relationships vary across ecologically distinct ocean biomes. Here we compiled in situ observations of EP, NPP, and SST and used statistical model selection theory to demonstrate significant biome-specific scaling relationships among these variables. Multiple statistically similar models yield a threefold variation in the globally integrated carbon flux (~4-12 Pg C yr-1) when applied to climatological satellite-derived NPP and SST. Simulated NPP and SST input variables from a 4×CO2 climate model experiment further show that biome-specific scaling alters the predicted response of EP to simulated increases of atmospheric CO2. These results highlight the need to better understand distinct pathways of carbon export across unique ecological biomes and may help guide proposed efforts for in situ observations of the ocean carbon cycle.

Engineering Escherichia coli for poly-(3-hydroxybutyrate) production guided by genome-scale metabolic network analysis.

PubMed

Zheng, Yangyang; Yuan, Qianqian; Yang, Xiaoyan; Ma, Hongwu

2017-11-01

Poly-(3-hydroxybutyrate) (P3HB) is a promising biodegradable plastic synthesized from acetyl-CoA. One important factor affecting the P3HB production cost is the P3HB yield. Through flux balance analysis of an extended genome-scale metabolic network of E. coli, we found that the introduction of non-oxidative glycolysis pathway (NOG), a previously reported pathway enabling complete carbon conservation, can increase the theoretical carbon yield from 67% to 89%, equivalent to the theoretical mass yield from 0.48g P3HB/g glucose to 0.64g P3HB/g glucose. Based on this analysis result, we introduced phosphoketolase and enhanced the NOG pathway in E. coli. The mass yield in the engineered strain was increased from 0.16g P3HB/g glucose to 0.24g P3HB/g glucose. We further overexpressed pntAB to enhance the NADPH availability and down-regulated TCA cycle to divert more acetyl-CoA toward P3HB. The final construct accumulated 5.7g/L P3HB and reached a carbon yield of 0.43 (a mass yield of 0.31g P3HB/g glucose) in shake flask cultures in shake flask cultures. The introduction of NOG pathway could also be useful for improving yields of many other biochemicals derived from acetyl-coA. Copyright © 2017 Elsevier Inc. All rights reserved.

Genome-Scale Metabolic Model for the Green Alga Chlorella vulgaris UTEX 395 Accurately Predicts Phenotypes under Autotrophic, Heterotrophic, and Mixotrophic Growth Conditions

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zuniga, Cristal; Li, Chien -Ting; Huelsman, Tyler

The green microalgae Chlorella vulgaris has been widely recognized as a promising candidate for biofuel production due to its ability to store high lipid content and its natural metabolic versatility. Compartmentalized genome-scale metabolic models constructed from genome sequences enable quantitative insight into the transport and metabolism of compounds within a target organism. These metabolic models have long been utilized to generate optimized design strategies for an improved production process. Here, we describe the reconstruction, validation, and application of a genome-scale metabolic model for C. vulgaris UTEX 395, iCZ843. The reconstruction represents the most comprehensive model for any eukaryotic photosynthetic organismmore » to date, based on the genome size and number of genes in the reconstruction. The highly curated model accurately predicts phenotypes under photoautotrophic, heterotrophic, and mixotrophic conditions. The model was validated against experimental data and lays the foundation for model-driven strain design and medium alteration to improve yield. Calculated flux distributions under different trophic conditions show that a number of key pathways are affected by nitrogen starvation conditions, including central carbon metabolism and amino acid, nucleotide, and pigment biosynthetic pathways. Moreover, model prediction of growth rates under various medium compositions and subsequent experimental validation showed an increased growth rate with the addition of tryptophan and methionine.« less

Genome-Scale Metabolic Model for the Green Alga Chlorella vulgaris UTEX 395 Accurately Predicts Phenotypes under Autotrophic, Heterotrophic, and Mixotrophic Growth Conditions

DOE PAGES

Zuniga, Cristal; Li, Chien -Ting; Huelsman, Tyler; ...

2016-07-02

The green microalgae Chlorella vulgaris has been widely recognized as a promising candidate for biofuel production due to its ability to store high lipid content and its natural metabolic versatility. Compartmentalized genome-scale metabolic models constructed from genome sequences enable quantitative insight into the transport and metabolism of compounds within a target organism. These metabolic models have long been utilized to generate optimized design strategies for an improved production process. Here, we describe the reconstruction, validation, and application of a genome-scale metabolic model for C. vulgaris UTEX 395, iCZ843. The reconstruction represents the most comprehensive model for any eukaryotic photosynthetic organismmore » to date, based on the genome size and number of genes in the reconstruction. The highly curated model accurately predicts phenotypes under photoautotrophic, heterotrophic, and mixotrophic conditions. The model was validated against experimental data and lays the foundation for model-driven strain design and medium alteration to improve yield. Calculated flux distributions under different trophic conditions show that a number of key pathways are affected by nitrogen starvation conditions, including central carbon metabolism and amino acid, nucleotide, and pigment biosynthetic pathways. Moreover, model prediction of growth rates under various medium compositions and subsequent experimental validation showed an increased growth rate with the addition of tryptophan and methionine.« less

Genome-Scale Metabolic Model for the Green Alga Chlorella vulgaris UTEX 395 Accurately Predicts Phenotypes under Autotrophic, Heterotrophic, and Mixotrophic Growth Conditions.

PubMed

Zuñiga, Cristal; Li, Chien-Ting; Huelsman, Tyler; Levering, Jennifer; Zielinski, Daniel C; McConnell, Brian O; Long, Christopher P; Knoshaug, Eric P; Guarnieri, Michael T; Antoniewicz, Maciek R; Betenbaugh, Michael J; Zengler, Karsten

2016-09-01

The green microalga Chlorella vulgaris has been widely recognized as a promising candidate for biofuel production due to its ability to store high lipid content and its natural metabolic versatility. Compartmentalized genome-scale metabolic models constructed from genome sequences enable quantitative insight into the transport and metabolism of compounds within a target organism. These metabolic models have long been utilized to generate optimized design strategies for an improved production process. Here, we describe the reconstruction, validation, and application of a genome-scale metabolic model for C. vulgaris UTEX 395, iCZ843. The reconstruction represents the most comprehensive model for any eukaryotic photosynthetic organism to date, based on the genome size and number of genes in the reconstruction. The highly curated model accurately predicts phenotypes under photoautotrophic, heterotrophic, and mixotrophic conditions. The model was validated against experimental data and lays the foundation for model-driven strain design and medium alteration to improve yield. Calculated flux distributions under different trophic conditions show that a number of key pathways are affected by nitrogen starvation conditions, including central carbon metabolism and amino acid, nucleotide, and pigment biosynthetic pathways. Furthermore, model prediction of growth rates under various medium compositions and subsequent experimental validation showed an increased growth rate with the addition of tryptophan and methionine. © 2016 American Society of Plant Biologists. All rights reserved.

Metabolic Engineering for Advanced Biofuels Production and Recent Advances Toward Commercialization

DOE PAGES

Meadows, Corey W.; Kang, Aram; Lee, Taek S.

2017-07-21

Research on renewable biofuels produced by microorganisms has enjoyed considerable advances in academic and industrial settings. As the renewable ethanol market approaches maturity, the demand is rising for the commercialization of more energy-dense fuel targets. Many strategies implemented in recent years have considerably increased the diversity and number of fuel targets that can be produced by microorganisms. Moreover, strain optimization for some of these fuel targets has ultimately led to their production at industrial scale. In this review, we discuss recent metabolic engineering approaches for augmenting biofuel production derived from alcohols, isoprenoids, and fatty acids in several microorganisms. In addition,more » we discuss successful commercialization ventures for each class of biofuel targets.« less

Metabolic Engineering for Advanced Biofuels Production and Recent Advances Toward Commercialization

DOE Office of Scientific and Technical Information (OSTI.GOV)

Meadows, Corey W.; Kang, Aram; Lee, Taek S.

Research on renewable biofuels produced by microorganisms has enjoyed considerable advances in academic and industrial settings. As the renewable ethanol market approaches maturity, the demand is rising for the commercialization of more energy-dense fuel targets. Many strategies implemented in recent years have considerably increased the diversity and number of fuel targets that can be produced by microorganisms. Moreover, strain optimization for some of these fuel targets has ultimately led to their production at industrial scale. In this review, we discuss recent metabolic engineering approaches for augmenting biofuel production derived from alcohols, isoprenoids, and fatty acids in several microorganisms. In addition,more » we discuss successful commercialization ventures for each class of biofuel targets.« less

Synthetic metabolic bypass for a metabolic toggle switch enhances acetyl-CoA supply for isopropanol production by Escherichia coli.

PubMed

Soma, Yuki; Yamaji, Taiki; Matsuda, Fumio; Hanai, Taizo

2017-05-01

Almost all synthetic pathways for biofuel production are designed to require endogenous metabolites in glycolysis, such as phosphoenolpyruvate, pyruvate, and acetyl-CoA. However, such metabolites are also required for bacterial cell growth. To reduce the metabolic imbalance between cell growth and target chemical production, we previously constructed a metabolic toggle switch (MTS) as a conditional flux redirection tool controlling metabolic flux of TCA cycle toward isopropanol production. This approach succeeded to improve the isopropanol production titer and yield while ensuring sufficient cell growth. However, excess accumulation of pyruvate, the precursor for acetyl-CoA synthesis, was also observed. In this study, for efficient conversation of pyruvate to acetyl-CoA (pyruvate oxidation), we designed a synthetic metabolic bypass composed of poxB and acs with the MTS for acetyl-CoA supply from the excess pyruvate. When this designed bypass was expressed at the appropriate expression level associated with the conditional metabolic flux redirection, pyruvate accumulation was prevented, and the isopropanol production titer and yield were improved. Final isopropanol production titer of strain harboring MTS with the synthetic metabolic bypass improved 4.4-fold compared with strain without metabolic flux regulation, and it was 1.3-fold higher than that of strain harboring the conventional MTS alone. Additionally, glucose consumption was also improved 1.7-fold compared with strain without metabolic flux regulation. On the other hand, introduction of the synthetic metabolic bypass alone showed no improvement in isopropanol production and glucose consumption. These results showed that the improvement in bio-production process caused by synergy between the MTS and the synthetic metabolic bypass. Copyright © 2016 The Society for Biotechnology, Japan. Published by Elsevier B.V. All rights reserved.

A protocol for generating a high-quality genome-scale metabolic reconstruction.

PubMed

Thiele, Ines; Palsson, Bernhard Ø

2010-01-01

Network reconstructions are a common denominator in systems biology. Bottom-up metabolic network reconstructions have been developed over the last 10 years. These reconstructions represent structured knowledge bases that abstract pertinent information on the biochemical transformations taking place within specific target organisms. The conversion of a reconstruction into a mathematical format facilitates a myriad of computational biological studies, including evaluation of network content, hypothesis testing and generation, analysis of phenotypic characteristics and metabolic engineering. To date, genome-scale metabolic reconstructions for more than 30 organisms have been published and this number is expected to increase rapidly. However, these reconstructions differ in quality and coverage that may minimize their predictive potential and use as knowledge bases. Here we present a comprehensive protocol describing each step necessary to build a high-quality genome-scale metabolic reconstruction, as well as the common trials and tribulations. Therefore, this protocol provides a helpful manual for all stages of the reconstruction process.

A protocol for generating a high-quality genome-scale metabolic reconstruction

PubMed Central

Thiele, Ines; Palsson, Bernhard Ø.

2011-01-01

Network reconstructions are a common denominator in systems biology. Bottom-up metabolic network reconstructions have developed over the past 10 years. These reconstructions represent structured knowledge-bases that abstract pertinent information on the biochemical transformations taking place within specific target organisms. The conversion of a reconstruction into a mathematical format facilitates myriad computational biological studies including evaluation of network content, hypothesis testing and generation, analysis of phenotypic characteristics, and metabolic engineering. To date, genome-scale metabolic reconstructions for more than 30 organisms have been published and this number is expected to increase rapidly. However, these reconstructions differ in quality and coverage that may minimize their predictive potential and use as knowledge-bases. Here, we present a comprehensive protocol describing each step necessary to build a high-quality genome-scale metabolic reconstruction as well as common trials and tribulations. Therefore, this protocol provides a helpful manual for all stages of the reconstruction process. PMID:20057383

Consistency Analysis of Genome-Scale Models of Bacterial Metabolism: A Metamodel Approach

PubMed Central

Ponce-de-Leon, Miguel; Calle-Espinosa, Jorge; Peretó, Juli; Montero, Francisco

2015-01-01

Genome-scale metabolic models usually contain inconsistencies that manifest as blocked reactions and gap metabolites. With the purpose to detect recurrent inconsistencies in metabolic models, a large-scale analysis was performed using a previously published dataset of 130 genome-scale models. The results showed that a large number of reactions (~22%) are blocked in all the models where they are present. To unravel the nature of such inconsistencies a metamodel was construed by joining the 130 models in a single network. This metamodel was manually curated using the unconnected modules approach, and then, it was used as a reference network to perform a gap-filling on each individual genome-scale model. Finally, a set of 36 models that had not been considered during the construction of the metamodel was used, as a proof of concept, to extend the metamodel with new biochemical information, and to assess its impact on gap-filling results. The analysis performed on the metamodel allowed to conclude: 1) the recurrent inconsistencies found in the models were already present in the metabolic database used during the reconstructions process; 2) the presence of inconsistencies in a metabolic database can be propagated to the reconstructed models; 3) there are reactions not manifested as blocked which are active as a consequence of some classes of artifacts, and; 4) the results of an automatic gap-filling are highly dependent on the consistency and completeness of the metamodel or metabolic database used as the reference network. In conclusion the consistency analysis should be applied to metabolic databases in order to detect and fill gaps as well as to detect and remove artifacts and redundant information. PMID:26629901

Metabolic analyses elucidate non-trivial gene targets for amplifying dihydroartemisinic acid production in yeast

PubMed Central

Misra, Ashish; Conway, Matthew F.; Johnnie, Joseph; Qureshi, Tabish M.; Lige, Bao; Derrick, Anne M.; Agbo, Eddy C.; Sriram, Ganesh

2013-01-01

Synthetic biology enables metabolic engineering of industrial microbes to synthesize value-added molecules. In this, a major challenge is the efficient redirection of carbon to the desired metabolic pathways. Pinpointing strategies toward this goal requires an in-depth investigation of the metabolic landscape of the organism, particularly primary metabolism, to identify precursor and cofactor availability for the target compound. The potent antimalarial therapeutic artemisinin and its precursors are promising candidate molecules for production in microbial hosts. Recent advances have demonstrated the production of artemisinin precursors in engineered yeast strains as an alternative to extraction from plants. We report the application of in silico and in vivo metabolic pathway analyses to identify metabolic engineering targets to improve the yield of the direct artemisinin precursor dihydroartemisinic acid (DHA) in yeast. First, in silico extreme pathway (ExPa) analysis identified NADPH-malic enzyme and the oxidative pentose phosphate pathway (PPP) as mechanisms to meet NADPH demand for DHA synthesis. Next, we compared key DHA-synthesizing ExPas to the metabolic flux distributions obtained from in vivo 13C metabolic flux analysis of a DHA-synthesizing strain. This comparison revealed that knocking out ethanol synthesis and overexpressing glucose-6-phosphate dehydrogenase in the oxidative PPP (gene YNL241C) or the NADPH-malic enzyme ME2 (YKL029C) are vital steps toward overproducing DHA. Finally, we employed in silico flux balance analysis and minimization of metabolic adjustment on a yeast genome-scale model to identify gene knockouts for improving DHA yields. The best strategy involved knockout of an oxaloacetate transporter (YKL120W) and an aspartate aminotransferase (YKL106W), and was predicted to improve DHA yields by 70-fold. Collectively, our work elucidates multiple non-trivial metabolic engineering strategies for improving DHA yield in yeast. PMID:23898325

Perspectives in metabolic engineering: understanding cellular regulation towards the control of metabolic routes.

PubMed

Zadran, Sohila; Levine, Raphael D

2013-01-01

Metabolic engineering seeks to redirect metabolic pathways through the modification of specific biochemical reactions or the introduction of new ones with the use of recombinant technology. Many of the chemicals synthesized via introduction of product-specific enzymes or the reconstruction of entire metabolic pathways into engineered hosts that can sustain production and can synthesize high yields of the desired product as yields of natural product-derived compounds are frequently low, and chemical processes can be both energy and material expensive; current endeavors have focused on using biologically derived processes as alternatives to chemical synthesis. Such economically favorable manufacturing processes pursue goals related to sustainable development and "green chemistry". Metabolic engineering is a multidisciplinary approach, involving chemical engineering, molecular biology, biochemistry, and analytical chemistry. Recent advances in molecular biology, genome-scale models, theoretical understanding, and kinetic modeling has increased interest in using metabolic engineering to redirect metabolic fluxes for industrial and therapeutic purposes. The use of metabolic engineering has increased the productivity of industrially pertinent small molecules, alcohol-based biofuels, and biodiesel. Here, we highlight developments in the practical and theoretical strategies and technologies available for the metabolic engineering of simple systems and address current limitations.

High-Throughput Microbore UPLC-MS Metabolic Phenotyping of Urine for Large-Scale Epidemiology Studies.

PubMed

Gray, Nicola; Lewis, Matthew R; Plumb, Robert S; Wilson, Ian D; Nicholson, Jeremy K

2015-06-05

A new generation of metabolic phenotyping centers are being created to meet the increasing demands of personalized healthcare, and this has resulted in a major requirement for economical, high-throughput metabonomic analysis by liquid chromatography-mass spectrometry (LC-MS). Meeting these new demands represents an emerging bioanalytical problem that must be solved if metabolic phenotyping is to be successfully applied to large clinical and epidemiological sample sets. Ultraperformance (UP)LC-MS-based metabolic phenotyping, based on 2.1 mm i.d. LC columns, enables comprehensive metabolic phenotyping but, when employed for the analysis of thousands of samples, results in high solvent usage. The use of UPLC-MS employing 1 mm i.d. columns for metabolic phenotyping rather than the conventional 2.1 mm i.d. methodology shows that the resulting optimized microbore method provided equivalent or superior performance in terms of peak capacity, sensitivity, and robustness. On average, we also observed, when using the microbore scale separation, an increase in response of 2-3 fold over that obtained with the standard 2.1 mm scale method. When applied to the analysis of human urine, the 1 mm scale method showed no decline in performance over the course of 1000 analyses, illustrating that microbore UPLC-MS represents a viable alternative to conventional 2.1 mm i.d. formats for routine large-scale metabolic profiling studies while also resulting in a 75% reduction in solvent usage. The modest increase in sensitivity provided by this methodology also offers the potential to either reduce sample consumption or increase the number of metabolite features detected with confidence due to the increased signal-to-noise ratios obtained. Implementation of this miniaturized UPLC-MS method of metabolic phenotyping results in clear analytical, economic, and environmental benefits for large-scale metabolic profiling studies with similar or improved analytical performance compared to

Engineering Cellular Metabolism.

PubMed

Nielsen, Jens; Keasling, Jay D

2016-03-10

Metabolic engineering is the science of rewiring the metabolism of cells to enhance production of native metabolites or to endow cells with the ability to produce new products. The potential applications of such efforts are wide ranging, including the generation of fuels, chemicals, foods, feeds, and pharmaceuticals. However, making cells into efficient factories is challenging because cells have evolved robust metabolic networks with hard-wired, tightly regulated lines of communication between molecular pathways that resist efforts to divert resources. Here, we will review the current status and challenges of metabolic engineering and will discuss how new technologies can enable metabolic engineering to be scaled up to the industrial level, either by cutting off the lines of control for endogenous metabolism or by infiltrating the system with disruptive, heterologous pathways that overcome cellular regulation. Copyright © 2016 Elsevier Inc. All rights reserved.

A model for allometric scaling of mammalian metabolism with ambient heat loss.

PubMed

Kwak, Ho Sang; Im, Hong G; Shim, Eun Bo

2016-03-01

Allometric scaling, which represents the dependence of biological traits or processes on body size, is a long-standing subject in biological science. However, there has been no study to consider heat loss to the ambient and an insulation layer representing mammalian skin and fur for the derivation of the scaling law of metabolism. A simple heat transfer model is proposed to analyze the allometry of mammalian metabolism. The present model extends existing studies by incorporating various external heat transfer parameters and additional insulation layers. The model equations were solved numerically and by an analytic heat balance approach. A general observation is that the present heat transfer model predicted the 2/3 surface scaling law, which is primarily attributed to the dependence of the surface area on the body mass. External heat transfer effects introduced deviations in the scaling law, mainly due to natural convection heat transfer, which becomes more prominent at smaller mass. These deviations resulted in a slight modification of the scaling exponent to a value < 2/3. The finding that additional radiative heat loss and the consideration of an outer insulation fur layer attenuate these deviation effects and render the scaling law closer to 2/3 provides in silico evidence for a functional impact of heat transfer mode on the allometric scaling law in mammalian metabolism.

A metabolite-centric view on flux distributions in genome-scale metabolic models

PubMed Central

2013-01-01

Background Genome-scale metabolic models are important tools in systems biology. They permit the in-silico prediction of cellular phenotypes via mathematical optimisation procedures, most importantly flux balance analysis. Current studies on metabolic models mostly consider reaction fluxes in isolation. Based on a recently proposed metabolite-centric approach, we here describe a set of methods that enable the analysis and interpretation of flux distributions in an integrated metabolite-centric view. We demonstrate how this framework can be used for the refinement of genome-scale metabolic models. Results We applied the metabolite-centric view developed here to the most recent metabolic reconstruction of Escherichia coli. By compiling the balance sheets of a small number of currency metabolites, we were able to fully characterise the energy metabolism as predicted by the model and to identify a possibility for model refinement in NADPH metabolism. Selected branch points were examined in detail in order to demonstrate how a metabolite-centric view allows identifying functional roles of metabolites. Fructose 6-phosphate aldolase and the sedoheptulose bisphosphate bypass were identified as enzymatic reactions that can carry high fluxes in the model but are unlikely to exhibit significant activity in vivo. Performing a metabolite essentiality analysis, unconstrained import and export of iron ions could be identified as potentially problematic for the quality of model predictions. Conclusions The system-wide analysis of split ratios and branch points allows a much deeper insight into the metabolic network than reaction-centric analyses. Extending an earlier metabolite-centric approach, the methods introduced here establish an integrated metabolite-centric framework for the interpretation of flux distributions in genome-scale metabolic networks that can complement the classical reaction-centric framework. Analysing fluxes and their metabolic context simultaneously opens

Constraining Genome-Scale Models to Represent the Bow Tie Structure of Metabolism for 13C Metabolic Flux Analysis

PubMed Central

Ando, David; Singh, Jahnavi; Keasling, Jay D.; García Martín, Héctor

2018-01-01

Determination of internal metabolic fluxes is crucial for fundamental and applied biology because they map how carbon and electrons flow through metabolism to enable cell function. 13C Metabolic Flux Analysis (13C MFA) and Two-Scale 13C Metabolic Flux Analysis (2S-13C MFA) are two techniques used to determine such fluxes. Both operate on the simplifying approximation that metabolic flux from peripheral metabolism into central “core” carbon metabolism is minimal, and can be omitted when modeling isotopic labeling in core metabolism. The validity of this “two-scale” or “bow tie” approximation is supported both by the ability to accurately model experimental isotopic labeling data, and by experimentally verified metabolic engineering predictions using these methods. However, the boundaries of core metabolism that satisfy this approximation can vary across species, and across cell culture conditions. Here, we present a set of algorithms that (1) systematically calculate flux bounds for any specified “core” of a genome-scale model so as to satisfy the bow tie approximation and (2) automatically identify an updated set of core reactions that can satisfy this approximation more efficiently. First, we leverage linear programming to simultaneously identify the lowest fluxes from peripheral metabolism into core metabolism compatible with the observed growth rate and extracellular metabolite exchange fluxes. Second, we use Simulated Annealing to identify an updated set of core reactions that allow for a minimum of fluxes into core metabolism to satisfy these experimental constraints. Together, these methods accelerate and automate the identification of a biologically reasonable set of core reactions for use with 13C MFA or 2S-13C MFA, as well as provide for a substantially lower set of flux bounds for fluxes into the core as compared with previous methods. We provide an open source Python implementation of these algorithms at https

Association of Lipid Accumulation Product with Cardio-Metabolic Risk Factors in Postmenopausal Women.

PubMed

Namazi Shabestari, Alireza; Asadi, Mojgan; Jouyandeh, Zahra; Qorbani, Mostafa; Kelishadi, Roya

2016-06-01

The lipid accumulation product is a novel, safe and inexpensive index of central lipid over accumulation based on waist circumference and fasting concentration of circulating triglycerides. This study was designed to investigate the ability of lipid accumulation product to predict Cardio-metabolic risk factors in postmenopausal women. In this Cross-sectional study, 264 postmenopausal women by using convenience sampling method were selected from menopause clinic in Tehran. Cardio-metabolic risk factors were measured, and lipid accumulation product (waist-58×triglycerides [nmol/L]) was calculated. Optimal cut-off point of lipid accumulation product for predicting metabolic syndrome was estimated by ROC (Receiver-operating characteristic) curve analysis. Metabolic syndrome was diagnosed in 41.2% of subjects. Optimal cut-off point of lipid accumulation product for predicting metabolic syndrome was 47.63 (sensitivity:75%; specificity:77.9%). High lipid accumulation product increases risk of all Cardio-metabolic risk factors except overweight, high Total Cholesterol, high Low Density Lipoprotein Cholesterol and high Fasting Blood Sugar in postmenopausal women. Our findings show that lipid accumulation product is associated with metabolic syndrome and some Cardio-metabolic risk factors Also lipid accumulation product may have been a useful tool for predicting cardiovascular disease and metabolic syndrome risk in postmenopausal women.

Mathematical modeling of unicellular microalgae and cyanobacteria metabolism for biofuel production.

PubMed

Baroukh, Caroline; Muñoz-Tamayo, Rafael; Bernard, Olivier; Steyer, Jean-Philippe

2015-06-01

The conversion of microalgae lipids and cyanobacteria carbohydrates into biofuels appears to be a promising source of renewable energy. This requires a thorough understanding of their carbon metabolism, supported by mathematical models, in order to optimize biofuel production. However, unlike heterotrophic microorganisms that utilize the same substrate as sources of energy and carbon, photoautotrophic microorganisms require light for energy and CO2 as carbon source. Furthermore, they are submitted to permanent fluctuating light environments due to outdoor cultivation or mixing inducing a flashing effect. Although, modeling these nonstandard organisms is a major challenge for which classical tools are often inadequate, this step remains a prerequisite towards efficient optimization of outdoor biofuel production at an industrial scale. Copyright © 2015 Elsevier Ltd. All rights reserved.

Optimal knockout strategies in genome-scale metabolic networks using particle swarm optimization.

PubMed

Nair, Govind; Jungreuthmayer, Christian; Zanghellini, Jürgen

2017-02-01

Knockout strategies, particularly the concept of constrained minimal cut sets (cMCSs), are an important part of the arsenal of tools used in manipulating metabolic networks. Given a specific design, cMCSs can be calculated even in genome-scale networks. We would however like to find not only the optimal intervention strategy for a given design but the best possible design too. Our solution (PSOMCS) is to use particle swarm optimization (PSO) along with the direct calculation of cMCSs from the stoichiometric matrix to obtain optimal designs satisfying multiple objectives. To illustrate the working of PSOMCS, we apply it to a toy network. Next we show its superiority by comparing its performance against other comparable methods on a medium sized E. coli core metabolic network. PSOMCS not only finds solutions comparable to previously published results but also it is orders of magnitude faster. Finally, we use PSOMCS to predict knockouts satisfying multiple objectives in a genome-scale metabolic model of E. coli and compare it with OptKnock and RobustKnock. PSOMCS finds competitive knockout strategies and designs compared to other current methods and is in some cases significantly faster. It can be used in identifying knockouts which will force optimal desired behaviors in large and genome scale metabolic networks. It will be even more useful as larger metabolic models of industrially relevant organisms become available.

Systems metabolic engineering: the creation of microbial cell factories by rational metabolic design and evolution.

PubMed

Furusawa, Chikara; Horinouchi, Takaaki; Hirasawa, Takashi; Shimizu, Hiroshi

2013-01-01

It is widely acknowledged that in order to establish sustainable societies, production processes should shift from petrochemical-based processes to bioprocesses. Because bioconversion technologies, in which biomass resources are converted to valuable materials, are preferable to processes dependent on fossil resources, the former should be further developed. The following two approaches can be adopted to improve cellular properties and obtain high productivity and production yield of target products: (1) optimization of cellular metabolic pathways involved in various bioprocesses and (2) creation of stress-tolerant cells that can be active even under severe stress conditions in the bioprocesses. Recent progress in omics analyses has facilitated the analysis of microorganisms based on bioinformatics data for molecular breeding and bioprocess development. Systems metabolic engineering is a new area of study, and it has been defined as a methodology in which metabolic engineering and systems biology are integrated to upgrade the designability of industrially useful microorganisms. This chapter discusses multi-omics analyses and rational design methods for molecular breeding. The first is an example of the rational design of metabolic networks for target production by flux balance analysis using genome-scale metabolic models. Recent progress in the development of genome-scale metabolic models and the application of these models to the design of desirable metabolic networks is also described in this example. The second is an example of evolution engineering with omics analyses for the creation of stress-tolerant microorganisms. Long-term culture experiments to obtain the desired phenotypes and omics analyses to identify the phenotypic changes are described here.

Metabolic engineering of Synechocystis sp. PCC 6803 for enhanced ethanol production based on flux balance analysis.

PubMed

Yoshikawa, Katsunori; Toya, Yoshihiro; Shimizu, Hiroshi

2017-05-01

Synechocystis sp. PCC 6803 is an attractive host for bio-ethanol production due to its ability to directly convert atmospheric carbon dioxide into ethanol using photosystems. To enhance ethanol production in Synechocystis sp. PCC 6803, metabolic engineering was performed based on in silico simulations, using the genome-scale metabolic model. Comprehensive reaction knockout simulations by flux balance analysis predicted that the knockout of NAD(P)H dehydrogenase enhanced ethanol production under photoautotrophic conditions, where ammonium is the nitrogen source. This deletion inhibits the re-oxidation of NAD(P)H, which is generated by ferredoxin-NADP + reductase and imposes re-oxidation in the ethanol synthesis pathway. The effect of deleting the ndhF1 gene, which encodes NADH dehydrogenase subunit 5, on ethanol production was experimentally evaluated using ethanol-producing strains of Synechocystis sp. PCC 6803. The ethanol titer of the ethanol-producing ∆ndhF1 strain increased by 145%, compared with that of the control strain.

Genome-scale reconstruction of the Streptococcus pyogenes M49 metabolic network reveals growth requirements and indicates potential drug targets.

PubMed

Levering, Jennifer; Fiedler, Tomas; Sieg, Antje; van Grinsven, Koen W A; Hering, Silvio; Veith, Nadine; Olivier, Brett G; Klett, Lara; Hugenholtz, Jeroen; Teusink, Bas; Kreikemeyer, Bernd; Kummer, Ursula

2016-08-20

Genome-scale metabolic models comprise stoichiometric relations between metabolites, as well as associations between genes and metabolic reactions and facilitate the analysis of metabolism. We computationally reconstructed the metabolic network of the lactic acid bacterium Streptococcus pyogenes M49. Initially, we based the reconstruction on genome annotations and already existing and curated metabolic networks of Bacillus subtilis, Escherichia coli, Lactobacillus plantarum and Lactococcus lactis. This initial draft was manually curated with the final reconstruction accounting for 480 genes associated with 576 reactions and 558 metabolites. In order to constrain the model further, we performed growth experiments of wild type and arcA deletion strains of S. pyogenes M49 in a chemically defined medium and calculated nutrient uptake and production fluxes. We additionally performed amino acid auxotrophy experiments to test the consistency of the model. The established genome-scale model can be used to understand the growth requirements of the human pathogen S. pyogenes and define optimal and suboptimal conditions, but also to describe differences and similarities between S. pyogenes and related lactic acid bacteria such as L. lactis in order to find strategies to reduce the growth of the pathogen and propose drug targets. Copyright © 2016 Elsevier B.V. All rights reserved.

Metabolic assessment of E. coli as a Biofactory for commercial products.

PubMed

Zhang, Xiaolin; Tervo, Christopher J; Reed, Jennifer L

2016-05-01

Metabolic engineering uses microorganisms to synthesize chemicals from renewable resources. Given the thousands of known metabolites, it is unclear what valuable chemicals could be produced by a microorganism and what native and heterologous reactions are needed for their synthesis. To answer these questions, a systematic computational assessment of Escherichia coli's potential ability to produce different chemicals was performed using an integrated metabolic model that included native E.coli reactions and known heterologous reactions. By adding heterologous reactions, a total of 1777 non-native products could theoretically be produced in E. coli under glucose minimal medium conditions, of which 279 non-native products have commercial applications. Synthesis pathways involving native and heterologous reactions were identified from eight central metabolic precursors to the 279 non-native commercial products. These pathways were used to evaluate the dependence on, and diversity of, native and heterologous reactions to produce each non-native commercial product, as well as to identify each product׳s closest central metabolic precursor. Analysis of the synthesis pathways (with 5 or fewer reaction steps) to non-native commercial products revealed that isopentenyl diphosphate, pyruvate, and oxaloacetate are the closest central metabolic precursors to the most non-native commercial products. Additionally, 4-hydroxybenzoate, tyrosine, and phenylalanine were found to be common precursors to a large number of non-native commercial products. Strains capable of producing high levels of these precursors could be further engineered to create strains capable of producing a variety of commercial non-native chemicals. Copyright © 2016 International Metabolic Engineering Society. Published by Elsevier Inc. All rights reserved.

Metabolic scaling and biodiversity of forests

NASA Astrophysics Data System (ADS)

Banavar, Jayanth

Forests are biologically diverse and play a critical role in the dynamics of earth-climate systems. A forest is a tremendously complex system comprising co-existing rooted trees of many species and many sizes and utilizing resources from the environment. The trees interact with each other and with their environment and the interactions are not precisely known. Using scaling ideas, we will present a theoretical framework for understanding the role of geometry in determining the metabolic rate of a tree and of a forest. The quantification of tropical tree biodiversity and their abundances is still an open and challenging problem. Using a global-scale compilation, we will present a method that allows one to predict, from local censuses, the biodiversity and patterns of species abundance at the whole forest scale. The method allows one to quantify the minimum percentage cover of the forest that should be sampled in order to have a precise prediction of the estimates of biodiversity and species abundances. Collaborators: Amos Maritan, Tommaso Anfodillo, Sandro Azaele, Marco Favretti, Marco Formentin, Jacopo Grilli, Samir Suweis, Anna Tovo, Igor Volkov.

Activation-specific metabolic requirements for NK cell IFN-γ production1

PubMed Central

Keppel, Molly P.; Topcagic, Nermina; Mah, Annelise Y.; Vogel, Tiphanie P.; Cooper, Megan A.

2014-01-01

There has been increasing recognition of the importance of cellular metabolism and metabolic substrates for the function and differentiation of immune cells. Here, for the first time, we investigate the metabolic requirements for production of IFN-γ by freshly isolated NK cells. Primary murine NK cells mainly utilize mitochondrial oxidative phosphorylation at rest and with short-term activation. Remarkably, we discovered significant differences in the metabolic requirements of murine NK cell IFN-γ production depending upon the activation signal. Stimulation of NK cell IFN-γ production was independent of glycolysis or mitochondrial oxidative phosphorylation when cells were activated with IL-12+IL-18. By contrast, stimulation via activating NK receptors required glucose-driven oxidative phosphorylation. Prolonged treatment with high-dose, but not low dose, IL-15 eliminated the metabolic requirement for receptor stimulation. In summary, this study demonstrates that metabolism provides an essential second signal for induction of IFN-γ production by activating NK cell receptors that can be reversed with prolonged high-dose IL-15 treatment. PMID:25595780

Reach‐scale river metabolism across contrasting sub‐catchment geologies: Effect of light and hydrology

PubMed Central

Attard, Karl M.; Binley, Andrew; Heppell, Catherine M.; Stahl, Henrik; Trimmer, Mark; Glud, Ronnie N.

2017-01-01

Abstract We investigated the seasonal dynamics of in‐stream metabolism at the reach scale (∼ 150 m) of headwaters across contrasting geological sub‐catchments: clay, Greensand, and Chalk of the upper River Avon (UK). Benthic metabolic activity was quantified by aquatic eddy co‐variance while water column activity was assessed by bottle incubations. Seasonal dynamics across reaches were specific for the three types of geologies. During the spring, all reaches were net autotrophic, with rates of up to 290 mmol C m−2 d−1 in the clay reach. During the remaining seasons, the clay and Greensand reaches were net heterotrophic, with peak oxygen consumption of 206 mmol m−2 d−1 during the autumn, while the Chalk reach was net heterotrophic only in winter. Overall, the water column alone still contributed to ∼ 25% of the annual respiration and primary production in all reaches. Net ecosystem metabolism (NEM) across seasons and reaches followed a general linear relationship with increasing stream light availability. Sub‐catchment specific NEM proved to be linearly related to the local hydrological connectivity, quantified as the ratio between base flow and stream discharge, and expressed on a timescale of 9 d on average. This timescale apparently represents the average period of hydrological imprint for carbon turnover within the reaches. Combining a general light response and sub‐catchment specific base flow ratio provided a robust functional relationship for predicting NEM at the reach scale. The novel approach proposed in this study can help facilitate spatial and temporal upscaling of riverine metabolism that may be applicable to a broader spectrum of catchments. PMID:29242670

Metabolic Engineering of Microalgal Based Biofuel Production: Prospects and Challenges.

PubMed

Banerjee, Chiranjib; Dubey, Kashyap K; Shukla, Pratyoosh

2016-01-01

The current scenario in renewable energy is focused on development of alternate and sustainable energy sources, amongst which microalgae stands as one of the promising feedstock for biofuel production. It is well known that microalgae generate much larger amounts of biofuels in a shorter time than other sources based on plant seeds. However, the greatest challenge in a transition to algae-based biofuel production is the various other complications involved in microalgal cultivation, its harvesting, concentration, drying and lipid extraction. Several green microalgae accumulate lipids, especially triacylglycerols (TAGs), which are main precursors in the production of lipid. The various aspects on metabolic pathway analysis of an oleaginous microalgae i.e., Chlamydomonas reinhardtii have elucidated some novel metabolically important genes and this enhances the lipid production in this microalgae. Adding to it, various other aspects in metabolic engineering using OptFlux and effectual bioprocess design also gives an interactive snapshot of enhancing lipid production which ultimately improvises the oil yield. This article reviews the current status of microalgal based technologies for biofuel production, bioreactor process design, flux analysis and it also provides various strategies to increase lipids accumulation via metabolic engineering.

Genome-Scale Metabolic Reconstructions and Theoretical Investigation of Methane Conversion in Methylomicrobium buryatense Strain 5G(B1)

DOE PAGES

de la Torre, Andrea; Metivier, Aisha; Chu, Frances; ...

2015-11-25

Methane-utilizing bacteria (methanotrophs) are capable of growth on methane and are attractive systems for bio-catalysis. However, the application of natural methanotrophic strains to large-scale production of value-added chemicals/biofuels requires a number of physiological and genetic alterations. An accurate metabolic model coupled with flux balance analysis can provide a solid interpretative framework for experimental data analyses and integration.

In silico method for modelling metabolism and gene product expression at genome scale

DOE Office of Scientific and Technical Information (OSTI.GOV)

Lerman, Joshua A.; Hyduke, Daniel R.; Latif, Haythem

2012-07-03

Transcription and translation use raw materials and energy generated metabolically to create the macromolecular machinery responsible for all cellular functions, including metabolism. A biochemically accurate model of molecular biology and metabolism will facilitate comprehensive and quantitative computations of an organism's molecular constitution as a function of genetic and environmental parameters. Here we formulate a model of metabolism and macromolecular expression. Prototyping it using the simple microorganism Thermotoga maritima, we show our model accurately simulates variations in cellular composition and gene expression. Moreover, through in silico comparative transcriptomics, the model allows the discovery of new regulons and improving the genome andmore » transcription unit annotations. Our method presents a framework for investigating molecular biology and cellular physiology in silico and may allow quantitative interpretation of multi-omics data sets in the context of an integrated biochemical description of an organism.« less

Adjustments in metabolic heat production by squirrel monkeys exposed to microwaves

DOE Office of Scientific and Technical Information (OSTI.GOV)

Adair, E.R.; Adams, B.W.

1982-04-01

The basic fact that microwave exposure can lower metabolic heat production has been previously demonstrated for the mouse by Ho and Edwards (1977) and for the rat by Phillips et al. (1975). The general conclusion drawn from both studies was that the metabolic reduction produced by microwave exposure was dose dependent. The present study extends the investigation into the effects of microwave exposure on metabolic heat production to a primate, the squirrel monkey. When squirrel monkeys are restrained in cool environments, body temperature is regulated by an increase in metabolic heat production. The results of the current study demonstrate thatmore » either brief or prolonged whole-body exposure to a microwave field will cause a reduction of this elevated heat production by an amount directly related to the microwave energy absorbed.« less

Solar photocatalytic oxidation of recalcitrant natural metabolic by-products of amoxicillin biodegradation.

PubMed

Pereira, João H O S; Reis, Ana C; Homem, Vera; Silva, José A; Alves, Arminda; Borges, Maria T; Boaventura, Rui A R; Vilar, Vítor J P; Nunes, Olga C

2014-11-15

The contamination of the aquatic environment by non-metabolized and metabolized antibiotic residues has brought the necessity of alternative treatment steps to current water decontamination technologies. This work assessed the feasibility of using a multistage treatment system for amoxicillin (AMX) spiked solutions combining: i) a biological treatment process using an enriched culture to metabolize AMX, with ii) a solar photocatalytic system to achieve the removal of the metabolized transformation products (TPs) identified via LC-MS, recalcitrant to further biological degradation. Firstly, a mixed culture (MC) was obtained through the enrichment of an activated sludge sample collected in an urban wastewater treatment plant (WWTP). Secondly, different aqueous matrices spiked with AMX were treated with the MC and the metabolic transformation products were identified. Thirdly, the efficiency of two solar assisted photocatalytic processes (TiO2/UV or Fe(3+)/Oxalate/H2O2/UV-Vis) was assessed in the degradation of the obtained TPs using a lab-scale prototype photoreactor equipped with a compound parabolic collector (CPC). Highest AMX specific biodegradation rates were obtained in buffer and urban wastewater (WW) media (0.10 ± 0.01 and 0.13 ± 0.07 g(AMX) g(biomass)(-1) h(-1), respectively). The resulting TPs, which no longer presented antibacterial activity, were identified as amoxicilloic acid (m/z = 384). The performance of the Fe(3+)/Oxalate/H2O2/UV-Vis system in the removal of the TPs from WW medium was superior to the TiO2/UV process (TPs no longer detected after 40 min (QUV = 2.6 kJ L(-1)), against incomplete TPs removal after 240 min (QUV = 14.9 kJ L(-1)), respectively). Copyright © 2014 Elsevier Ltd. All rights reserved.

Intraspecific variation in the metabolic scaling exponent in ectotherms: testing the effect of latitudinal cline, ontogeny and transgenerational change in the land snail Cornu aspersum.

PubMed

Gaitán-Espitia, Juan Diego; Bruning, Andrea; Mondaca, Fredy; Nespolo, Roberto F

2013-06-01

The strong dependence of metabolic rates on body mass has attracted the interest of ecological physiologists, as it has important implications to many aspects of biology including species variations in body size, the evolution of life history, and the structure and function of biological communities. The great diversity of observed scaling exponents has led some authors to conclude that there is no single universal scaling exponent, but instead it ranges from 2/3 to 1. Most of the telling evidence against the universality of power scaling exponents comes from ontogenetic changes. Nevertheless, there could be other sources of phenotypic variation that influence this allometric relationship at least at the intraspecific level. In order to explore the general concept of the metabolic scaling in terrestrial molluscs we tested the role of several biological and methodological sources of variation on the empirically estimated scaling exponent. Specifically, we measured a proxy of metabolic rate (CO(2) production) in 421 individuals, during three generations, in three different populations. Additionally, we measured this scaling relationship in 208 individuals at five developmental stages. Our results suggest that the metabolic scaling exponent at the intraspecific level does not have a single stationary value, but instead it shows some degree of variation across geographic distribution, transgenerational change and ontogenetic stages. The major differences in the metabolic scaling exponent that we found were at different developmental stages of snails, because ontogeny involves increases in size at different rates, which in turn, generate differential energy demands. Copyright © 2013 Elsevier Inc. All rights reserved.

MapMaker and PathTracer for tracking carbon in genome-scale metabolic models

PubMed Central

Tervo, Christopher J.; Reed, Jennifer L.

2016-01-01

Constraint-based reconstruction and analysis (COBRA) modeling results can be difficult to interpret given the large numbers of reactions in genome-scale models. While paths in metabolic networks can be found, existing methods are not easily combined with constraint-based approaches. To address this limitation, two tools (MapMaker and PathTracer) were developed to find paths (including cycles) between metabolites, where each step transfers carbon from reactant to product. MapMaker predicts carbon transfer maps (CTMs) between metabolites using only information on molecular formulae and reaction stoichiometry, effectively determining which reactants and products share carbon atoms. MapMaker correctly assigned CTMs for over 97% of the 2,251 reactions in an Escherichia coli metabolic model (iJO1366). Using CTMs as inputs, PathTracer finds paths between two metabolites. PathTracer was applied to iJO1366 to investigate the importance of using CTMs and COBRA constraints when enumerating paths, to find active and high flux paths in flux balance analysis (FBA) solutions, to identify paths for putrescine utilization, and to elucidate a potential CO2 fixation pathway in E. coli. These results illustrate how MapMaker and PathTracer can be used in combination with constraint-based models to identify feasible, active, and high flux paths between metabolites. PMID:26771089

Metabolic response to air temperature and wind in day-old mallards and a standard operative temperature scale

USGS Publications Warehouse

Bakken, G.S.; Reynolds, P.S.; Kenow, K.P.; Korschgen, C.E.; Boysen, A.F.

1999-01-01

Most duckling mortality occurs during the week following hatching and is often associated with cold, windy, wet weather and scattering of the brood. We estimated the thermoregulatory demands imposed by cold, windy weather on isolated 1-d-old mallard (Anas platyrhynchos) ducklings resting in cover. We measured O-2 consumption and evaporative water loss at air temperatures from 5 degrees to 25 degrees C and wind speeds of 0.1, 0.2, 0.5, and 1.0 mis. Metabolic heat production increased as wind increased or temperature decreased but was less sensitive to wind than that of either adult passerines or small mammals. Evaporative heat loss ranged from 5% to 17% of heat production. Evaporative heal loss and the ratio of evaporative heat loss to metabolic heat production was significantly lower in rest phase. These data were used to define a standard operative temperature (T-es) scale for night or heavy overcast conditions. An increase of wind speed from 0.1 to 1 mis decreased T-es by 3 degrees-5 degrees C.

A global approach to analysis and interpretation of metabolic data for plant natural product discovery.

PubMed

Hur, Manhoi; Campbell, Alexis Ann; Almeida-de-Macedo, Marcia; Li, Ling; Ransom, Nick; Jose, Adarsh; Crispin, Matt; Nikolau, Basil J; Wurtele, Eve Syrkin

2013-04-01

Discovering molecular components and their functionality is key to the development of hypotheses concerning the organization and regulation of metabolic networks. The iterative experimental testing of such hypotheses is the trajectory that can ultimately enable accurate computational modelling and prediction of metabolic outcomes. This information can be particularly important for understanding the biology of natural products, whose metabolism itself is often only poorly defined. Here, we describe factors that must be in place to optimize the use of metabolomics in predictive biology. A key to achieving this vision is a collection of accurate time-resolved and spatially defined metabolite abundance data and associated metadata. One formidable challenge associated with metabolite profiling is the complexity and analytical limits associated with comprehensively determining the metabolome of an organism. Further, for metabolomics data to be efficiently used by the research community, it must be curated in publicly available metabolomics databases. Such databases require clear, consistent formats, easy access to data and metadata, data download, and accessible computational tools to integrate genome system-scale datasets. Although transcriptomics and proteomics integrate the linear predictive power of the genome, the metabolome represents the nonlinear, final biochemical products of the genome, which results from the intricate system(s) that regulate genome expression. For example, the relationship of metabolomics data to the metabolic network is confounded by redundant connections between metabolites and gene-products. However, connections among metabolites are predictable through the rules of chemistry. Therefore, enhancing the ability to integrate the metabolome with anchor-points in the transcriptome and proteome will enhance the predictive power of genomics data. We detail a public database repository for metabolomics, tools and approaches for statistical analysis

A global approach to analysis and interpretation of metabolic data for plant natural product discovery†

PubMed Central

Hur, Manhoi; Campbell, Alexis Ann; Almeida-de-Macedo, Marcia; Li, Ling; Ransom, Nick; Jose, Adarsh; Crispin, Matt; Nikolau, Basil J.

2013-01-01

Discovering molecular components and their functionality is key to the development of hypotheses concerning the organization and regulation of metabolic networks. The iterative experimental testing of such hypotheses is the trajectory that can ultimately enable accurate computational modelling and prediction of metabolic outcomes. This information can be particularly important for understanding the biology of natural products, whose metabolism itself is often only poorly defined. Here, we describe factors that must be in place to optimize the use of metabolomics in predictive biology. A key to achieving this vision is a collection of accurate time-resolved and spatially defined metabolite abundance data and associated metadata. One formidable challenge associated with metabolite profiling is the complexity and analytical limits associated with comprehensively determining the metabolome of an organism. Further, for metabolomics data to be efficiently used by the research community, it must be curated in publically available metabolomics databases. Such databases require clear, consistent formats, easy access to data and metadata, data download, and accessible computational tools to integrate genome system-scale datasets. Although transcriptomics and proteomics integrate the linear predictive power of the genome, the metabolome represents the nonlinear, final biochemical products of the genome, which results from the intricate system(s) that regulate genome expression. For example, the relationship of metabolomics data to the metabolic network is confounded by redundant connections between metabolites and gene-products. However, connections among metabolites are predictable through the rules of chemistry. Therefore, enhancing the ability to integrate the metabolome with anchor-points in the transcriptome and proteome will enhance the predictive power of genomics data. We detail a public database repository for metabolomics, tools and approaches for statistical

Toward the automated generation of genome-scale metabolic networks in the SEED.

PubMed

DeJongh, Matthew; Formsma, Kevin; Boillot, Paul; Gould, John; Rycenga, Matthew; Best, Aaron

2007-04-26

Current methods for the automated generation of genome-scale metabolic networks focus on genome annotation and preliminary biochemical reaction network assembly, but do not adequately address the process of identifying and filling gaps in the reaction network, and verifying that the network is suitable for systems level analysis. Thus, current methods are only sufficient for generating draft-quality networks, and refinement of the reaction network is still largely a manual, labor-intensive process. We have developed a method for generating genome-scale metabolic networks that produces substantially complete reaction networks, suitable for systems level analysis. Our method partitions the reaction space of central and intermediary metabolism into discrete, interconnected components that can be assembled and verified in isolation from each other, and then integrated and verified at the level of their interconnectivity. We have developed a database of components that are common across organisms, and have created tools for automatically assembling appropriate components for a particular organism based on the metabolic pathways encoded in the organism's genome. This focuses manual efforts on that portion of an organism's metabolism that is not yet represented in the database. We have demonstrated the efficacy of our method by reverse-engineering and automatically regenerating the reaction network from a published genome-scale metabolic model for Staphylococcus aureus. Additionally, we have verified that our method capitalizes on the database of common reaction network components created for S. aureus, by using these components to generate substantially complete reconstructions of the reaction networks from three other published metabolic models (Escherichia coli, Helicobacter pylori, and Lactococcus lactis). We have implemented our tools and database within the SEED, an open-source software environment for comparative genome annotation and analysis. Our method sets the

Patterns of Metabolite Changes Identified from Large-Scale Gene Perturbations in Arabidopsis Using a Genome-Scale Metabolic Network1[OPEN

PubMed Central

Kim, Taehyong; Dreher, Kate; Nilo-Poyanco, Ricardo; Lee, Insuk; Fiehn, Oliver; Lange, Bernd Markus; Nikolau, Basil J.; Sumner, Lloyd; Welti, Ruth; Wurtele, Eve S.; Rhee, Seung Y.

2015-01-01

Metabolomics enables quantitative evaluation of metabolic changes caused by genetic or environmental perturbations. However, little is known about how perturbing a single gene changes the metabolic system as a whole and which network and functional properties are involved in this response. To answer this question, we investigated the metabolite profiles from 136 mutants with single gene perturbations of functionally diverse Arabidopsis (Arabidopsis thaliana) genes. Fewer than 10 metabolites were changed significantly relative to the wild type in most of the mutants, indicating that the metabolic network was robust to perturbations of single metabolic genes. These changed metabolites were closer to each other in a genome-scale metabolic network than expected by chance, supporting the notion that the genetic perturbations changed the network more locally than globally. Surprisingly, the changed metabolites were close to the perturbed reactions in only 30% of the mutants of the well-characterized genes. To determine the factors that contributed to the distance between the observed metabolic changes and the perturbation site in the network, we examined nine network and functional properties of the perturbed genes. Only the isozyme number affected the distance between the perturbed reactions and changed metabolites. This study revealed patterns of metabolic changes from large-scale gene perturbations and relationships between characteristics of the perturbed genes and metabolic changes. PMID:25670818

Prospects of microbial cell factories developed through systems metabolic engineering.

PubMed

Gustavsson, Martin; Lee, Sang Yup

2016-09-01

While academic-level studies on metabolic engineering of microorganisms for production of chemicals and fuels are ever growing, a significantly lower number of such production processes have reached commercial-scale. In this work, we review the challenges associated with moving from laboratory-scale demonstration of microbial chemical or fuel production to actual commercialization, focusing on key requirements on the production organism that need to be considered during the metabolic engineering process. Metabolic engineering strategies should take into account techno-economic factors such as the choice of feedstock, the product yield, productivity and titre, and the cost effectiveness of midstream and downstream processes. Also, it is important to develop an industrial strain through metabolic engineering for pathway construction and flux optimization together with increasing tolerance to products and inhibitors present in the feedstock, and ensuring genetic stability and strain robustness under actual fermentation conditions. © 2016 The Authors. Microbial Biotechnology published by John Wiley & Sons Ltd and Society for Applied Microbiology.

Size matters: plasticity in metabolic scaling shows body-size may modulate responses to climate change.

PubMed

Carey, Nicholas; Sigwart, Julia D

2014-08-01

Variability in metabolic scaling in animals, the relationship between metabolic rate ( R: ) and body mass ( M: ), has been a source of debate and controversy for decades. R: is proportional to MB: , the precise value of B: much debated, but historically considered equal in all organisms. Recent metabolic theory, however, predicts B: to vary among species with ecology and metabolic level, and may also vary within species under different abiotic conditions. Under climate change, most species will experience increased temperatures, and marine organisms will experience the additional stressor of decreased seawater pH ('ocean acidification'). Responses to these environmental changes are modulated by myriad species-specific factors. Body-size is a fundamental biological parameter, but its modulating role is relatively unexplored. Here, we show that changes to metabolic scaling reveal asymmetric responses to stressors across body-size ranges; B: is systematically decreased under increasing temperature in three grazing molluscs, indicating smaller individuals were more responsive to warming. Larger individuals were, however, more responsive to reduced seawater pH in low temperatures. These alterations to the allometry of metabolism highlight abiotic control of metabolic scaling, and indicate that responses to climate warming and ocean acidification may be modulated by body-size. © 2014 The Author(s) Published by the Royal Society. All rights reserved.

Multiscale Metabolic Modeling of C4 Plants: Connecting Nonlinear Genome-Scale Models to Leaf-Scale Metabolism in Developing Maize Leaves

PubMed Central

Bogart, Eli; Myers, Christopher R.

2016-01-01

C4 plants, such as maize, concentrate carbon dioxide in a specialized compartment surrounding the veins of their leaves to improve the efficiency of carbon dioxide assimilation. Nonlinear relationships between carbon dioxide and oxygen levels and reaction rates are key to their physiology but cannot be handled with standard techniques of constraint-based metabolic modeling. We demonstrate that incorporating these relationships as constraints on reaction rates and solving the resulting nonlinear optimization problem yields realistic predictions of the response of C4 systems to environmental and biochemical perturbations. Using a new genome-scale reconstruction of maize metabolism, we build an 18000-reaction, nonlinearly constrained model describing mesophyll and bundle sheath cells in 15 segments of the developing maize leaf, interacting via metabolite exchange, and use RNA-seq and enzyme activity measurements to predict spatial variation in metabolic state by a novel method that optimizes correlation between fluxes and expression data. Though such correlations are known to be weak in general, we suggest that developmental gradients may be particularly suited to the inference of metabolic fluxes from expression data, and we demonstrate that our method predicts fluxes that achieve high correlation with the data, successfully capture the experimentally observed base-to-tip transition between carbon-importing tissue and carbon-exporting tissue, and include a nonzero growth rate, in contrast to prior results from similar methods in other systems. PMID:26990967

Allometric Scaling and Cell Ratios in Multi-Organ in vitro Models of Human Metabolism.

PubMed

Ucciferri, Nadia; Sbrana, Tommaso; Ahluwalia, Arti

2014-01-01

Intelligent in vitro models able to recapitulate the physiological interactions between tissues in the body have enormous potential as they enable detailed studies on specific two-way or higher order tissue communication. These models are the first step toward building an integrated picture of systemic metabolism and signaling in physiological or pathological conditions. However, the rational design of in vitro models of cell-cell or cell-tissue interaction is difficult as quite often cell culture experiments are driven by the device used, rather than by design considerations. Indeed, very little research has been carried out on in vitro models of metabolism connecting different cell or tissue types in a physiologically and metabolically relevant manner. Here, we analyze the physiological relationship between cells, cell metabolism, and exchange in the human body using allometric rules, downscaling them to an organ-on-a-plate device. In particular, in order to establish appropriate cell ratios in the system in a rational manner, two different allometric scaling models (cell number scaling model and metabolic and surface scaling model) are proposed and applied to a two compartment model of hepatic-vascular metabolic cross-talk. The theoretical scaling studies illustrate that the design and hence relevance of multi-organ models is principally determined by experimental constraints. Two experimentally feasible model configurations are then implemented in a multi-compartment organ-on-a-plate device. An analysis of the metabolic response of the two configurations demonstrates that their glucose and lipid balance is quite different, with only one of the two models recapitulating physiological-like homeostasis. In conclusion, not only do cross-talk and physical stimuli play an important role in in vitro models, but the numeric relationship between cells is also crucial to recreate in vitro interactions, which can be extrapolated to the in vivo reality.

Allometric Scaling and Cell Ratios in Multi-Organ in vitro Models of Human Metabolism

DOE Office of Scientific and Technical Information (OSTI.GOV)

Ucciferri, Nadia; Interdepartmental Research Center “E. Piaggio”, University of Pisa, Pisa; Sbrana, Tommaso

2014-12-17

Intelligent in vitro models able to recapitulate the physiological interactions between tissues in the body have enormous potential as they enable detailed studies on specific two-way or higher order tissue communication. These models are the first step toward building an integrated picture of systemic metabolism and signaling in physiological or pathological conditions. However, the rational design of in vitro models of cell–cell or cell–tissue interaction is difficult as quite often cell culture experiments are driven by the device used, rather than by design considerations. Indeed, very little research has been carried out on in vitro models of metabolism connecting differentmore » cell or tissue types in a physiologically and metabolically relevant manner. Here, we analyze the physiological relationship between cells, cell metabolism, and exchange in the human body using allometric rules, downscaling them to an organ-on-a-plate device. In particular, in order to establish appropriate cell ratios in the system in a rational manner, two different allometric scaling models (cell number scaling model and metabolic and surface scaling model) are proposed and applied to a two compartment model of hepatic-vascular metabolic cross-talk. The theoretical scaling studies illustrate that the design and hence relevance of multi-organ models is principally determined by experimental constraints. Two experimentally feasible model configurations are then implemented in a multi-compartment organ-on-a-plate device. An analysis of the metabolic response of the two configurations demonstrates that their glucose and lipid balance is quite different, with only one of the two models recapitulating physiological-like homeostasis. In conclusion, not only do cross-talk and physical stimuli play an important role in in vitro models, but the numeric relationship between cells is also crucial to recreate in vitro interactions, which can be extrapolated to the in vivo reality.« less

Dissecting metabolic behavior of lipid over-producing strain of Mucor circinelloides through genome-scale metabolic network and multi-level data integration.

PubMed

Vongsangnak, Wanwipa; Kingkaw, Amornthep; Yang, Junhuan; Song, Yuanda; Laoteng, Kobkul

2018-09-05

Lipid accumulation is an important cellular process of oleaginous microorganisms. To dissect metabolic behavior of oleaginous Zygomycetes, the lipid over-producing strain, Mucor circinelloides WJ11, was subjected for omics-scale analysis. The genome annotation was improved and used for construction of genome-scale metabolic network of WJ11 strain. Then, the quality of the metabolic network was enhanced by incorporating gene and protein expression data. In addition to the known oleaginous genes, our results showed a number of newly identified unique genes of WJ11 strain, which involved in central carbon metabolism, lipid, amino acid and nitrogen metabolisms. The systematic compilations indicated the additional metabolic routes with the involvement in supplying precursors (acetyl-CoA, NADPH and fatty acyl substrate) for fatty acid and lipid biosynthesis. Interestingly, amino acid metabolism played a substantial role in responsive mechanism of the fungal cells to nutrient imbalance circumstance through lipogenesis as the finding of reporter metabolites (l-methionine, l-glutamate, l-aspartate, l-asparagine and l-glutamine) at lipid-accumulating stage. The cooperative function of certain lipid-degrading enzymes at the particular growth stage was elucidated by integrating the metabolic networks with gene expression data. The unique feature of carotenoid biosynthetic route in WJ11 strain was also identified by protein domain analysis. Taken together, there were cross-functional metabolisms in regulating lipid biosynthesis and retaining high level of cellular lipids in the representative of lipid over-producing strains. Copyright © 2018 Elsevier B.V. All rights reserved.

Metabolic engineering for the production of 1,3-propanediol

DOE Office of Scientific and Technical Information (OSTI.GOV)

Cameron, D.C.; Tong, I.T., Skraly, F.A.

1993-12-31

Metabolic engineering involves the use of recombinant DNA techniques for the modification of intermediary metabolic pathways. Microorganisms have recently been engineered to produce compounds such as indigo, ethanol, fatty acids and polyhydroxyalkanoates. As a model system for research in metabolic engineering, the authors have constructed a strain of the bacterium Escherichia coli, that is able to produce 1,3-propanediol (1,3-PD) from glycerol. This strain contains the genes for the glycerol deydratase and the 1,3-PD oxidoreductase from Klebsiella pneumoniae. The authors have also investigated genetic and environmental strategies for improving the yield and productivity of 1,3-PD by the engineered organism. In additionmore » to being a useful model system, 1,3-PD production is of current practical interest. First 1,3-PD (also known as trimethylene glycol) and 1,4-butanediol, the more readily available diols. Second, the volume of feedstock (glycerol) is expected to grow, as it is a by-product of the production of polyglycoside surfactants and biodiesel fluids.« less

Deviation from symmetrically self-similar branching in trees predicts altered hydraulics, mechanics, light interception and metabolic scaling.

PubMed

Smith, Duncan D; Sperry, John S; Enquist, Brian J; Savage, Van M; McCulloh, Katherine A; Bentley, Lisa P

2014-01-01

The West, Brown, Enquist (WBE) model derives symmetrically self-similar branching to predict metabolic scaling from hydraulic conductance, K, (a metabolism proxy) and tree mass (or volume, V). The original prediction was Kα V(0.75). We ask whether trees differ from WBE symmetry and if it matters for plant function and scaling. We measure tree branching and model how architecture influences K, V, mechanical stability, light interception and metabolic scaling. We quantified branching architecture by measuring the path fraction, Pf : mean/maximum trunk-to-twig pathlength. WBE symmetry produces the maximum, Pf = 1.0. We explored tree morphospace using a probability-based numerical model constrained only by biomechanical principles. Real tree Pf ranged from 0.930 (nearly symmetric) to 0.357 (very asymmetric). At each modeled tree size, a reduction in Pf led to: increased K; decreased V; increased mechanical stability; and decreased light absorption. When Pf was ontogenetically constant, strong asymmetry only slightly steepened metabolic scaling. The Pf ontogeny of real trees, however, was 'U' shaped, resulting in size-dependent metabolic scaling that exceeded 0.75 in small trees before falling below 0.65. Architectural diversity appears to matter considerably for whole-tree hydraulics, mechanics, photosynthesis and potentially metabolic scaling. Optimal architectures likely exist that maximize carbon gain per structural investment. © 2013 The Authors. New Phytologist © 2013 New Phytologist Trust.

Fermentative Production of the Diamine Putrescine: System Metabolic Engineering of Corynebacterium Glutamicum

PubMed Central

Nguyen, Anh Q. D.; Schneider, Jens; Reddy, Gajendar Komati; Wendisch, Volker F.

2015-01-01

Corynebacterium glutamicum shows great potential for the production of the glutamate-derived diamine putrescine, a monomeric compound of polyamides. A genome-scale stoichiometric model of a C. glutamicum strain with reduced ornithine transcarbamoylase activity, derepressed arginine biosynthesis, and an anabolic plasmid-addiction system for heterologous expression of E. coli ornithine decarboxylase gene speC was investigated by flux balance analysis with respect to its putrescine production potential. Based on these simulations, enhancing glycolysis and anaplerosis by plasmid-borne overexpression of the genes for glyceraldehyde 3-phosphate dehydrogenase and pyruvate carboxylase as well as reducing 2-oxoglutarate dehydrogenase activity were chosen as targets for metabolic engineering. Changing the translational start codon of the chromosomal gene for 2-oxoglutarate dehydrogenase subunit E1o to the less preferred TTG and changing threonine 15 of OdhI to alanine reduced 2-oxoglutarate dehydrogenase activity about five fold and improved putrescine titers by 28%. Additional engineering steps improved further putrescine production with the largest contributions from preventing the formation of the by-product N-acetylputrescine by deletion of spermi(di)ne N-acetyltransferase gene snaA and from overexpression of the gene for a feedback-resistant N-acetylglutamate kinase variant. The resulting C. glutamicum strain NA6 obtained by systems metabolic engineering accumulated two fold more putrescine than the base strain, i.e., 58.1 ± 0.2 mM, and showed a specific productivity of 0.045 g·g−1·h−1 and a yield on glucose of 0.26 g·g−1. PMID:25919117

Thermoneutral zone and scaling of metabolic rate on body mass in small mammals

NASA Technical Reports Server (NTRS)

Pace, N.; Rahlmann, D. F.

1983-01-01

A 4-species animal model suitable for experimental study of the effect of change in gravitational loading on the scale relationship between metabolic rate and total body mass is used to study the effect of temperature on metabolic rate in six male animals, 8-10 months of age, of each of the four species in the ambient temperature range 20-36 C. The measurements taken permitted partitioning of total body heat output into sensible heat loss by radiation, conduction and convection, and into latent heat loss by evaporation of water from the body surface. It is shown that the condition of thermoneutrality is important for metabolic scale effect studies, and that the thermoneutral zone for the species considered here is a narrow one.

Metabolic profiling reveals that time related physiological changes in mammalian cell perfusion cultures are bioreactor scale independent.

PubMed

Vernardis, Spyros I; Goudar, Chetan T; Klapa, Maria I

2013-09-01

Metabolic profiling was used to characterize the time course of cell physiology both in laboratory- and manufacturing-scale mammalian cell perfusion cultures. Two independent experiments were performed involving three vials from the same BHK cell bank, used to inoculate three laboratory-scale bioreactors, from which four manufacturing-scale cultures were initiated. It was shown that metabolomic analysis can indeed enhance the prime variable dataset for the monitoring of perfusion cultures by providing a higher resolution view of the metabolic state. Metabolic profiles could capture physiological state shifts over the course of the perfusion cultures and indicated a metabolic "signature" of the phase transitions, which was not observable from prime variable data. Specifically, the vast majority of metabolites had lower concentrations in the middle compared to the other two phases. Notably, metabolomics provided orthogonal (to prime variables) evidence that all cultures followed this same metabolic state shift with cell age, independently of bioreactor scale. © 2013 Elsevier Inc. All rights reserved.

Unique attributes of cyanobacterial metabolism revealed by improved genome-scale metabolic modeling and essential gene analysis

DOE PAGES

Broddrick, Jared T.; Rubin, Benjamin E.; Welkie, David G.; ...

2016-12-20

The model cyanobacterium, Synechococcus elongatus PCC 7942, is a genetically tractable obligate phototroph that is being developed for the bioproduction of high-value chemicals. Genome-scale models (GEMs) have been successfully used to assess and engineer cellular metabolism; however, GEMs of phototrophic metabolism have been limited by the lack of experimental datasets for model validation and the challenges of incorporating photon uptake. In this paper, we develop a GEM of metabolism in S. elongatus using random barcode transposon site sequencing (RB-TnSeq) essential gene and physiological data specific to photoautotrophic metabolism. The model explicitly describes photon absorption and accounts for shading, resulting inmore » the characteristic linear growth curve of photoautotrophs. GEM predictions of gene essentiality were compared with data obtained from recent dense-transposon mutagenesis experiments. This dataset allowed major improvements to the accuracy of the model. Furthermore, discrepancies between GEM predictions and the in vivo dataset revealed biological characteristics, such as the importance of a truncated, linear TCA pathway, low flux toward amino acid synthesis from photorespiration, and knowledge gaps within nucleotide metabolism. Finally, coupling of strong experimental support and photoautotrophic modeling methods thus resulted in a highly accurate model of S. elongatus metabolism that highlights previously unknown areas of S. elongatus biology.« less

Unique attributes of cyanobacterial metabolism revealed by improved genome-scale metabolic modeling and essential gene analysis

PubMed Central

Broddrick, Jared T.; Rubin, Benjamin E.; Welkie, David G.; Du, Niu; Mih, Nathan; Diamond, Spencer; Lee, Jenny J.; Golden, Susan S.; Palsson, Bernhard O.

2016-01-01

The model cyanobacterium, Synechococcus elongatus PCC 7942, is a genetically tractable obligate phototroph that is being developed for the bioproduction of high-value chemicals. Genome-scale models (GEMs) have been successfully used to assess and engineer cellular metabolism; however, GEMs of phototrophic metabolism have been limited by the lack of experimental datasets for model validation and the challenges of incorporating photon uptake. Here, we develop a GEM of metabolism in S. elongatus using random barcode transposon site sequencing (RB-TnSeq) essential gene and physiological data specific to photoautotrophic metabolism. The model explicitly describes photon absorption and accounts for shading, resulting in the characteristic linear growth curve of photoautotrophs. GEM predictions of gene essentiality were compared with data obtained from recent dense-transposon mutagenesis experiments. This dataset allowed major improvements to the accuracy of the model. Furthermore, discrepancies between GEM predictions and the in vivo dataset revealed biological characteristics, such as the importance of a truncated, linear TCA pathway, low flux toward amino acid synthesis from photorespiration, and knowledge gaps within nucleotide metabolism. Coupling of strong experimental support and photoautotrophic modeling methods thus resulted in a highly accurate model of S. elongatus metabolism that highlights previously unknown areas of S. elongatus biology. PMID:27911809

Unique attributes of cyanobacterial metabolism revealed by improved genome-scale metabolic modeling and essential gene analysis

DOE Office of Scientific and Technical Information (OSTI.GOV)

Broddrick, Jared T.; Rubin, Benjamin E.; Welkie, David G.

The model cyanobacterium, Synechococcus elongatus PCC 7942, is a genetically tractable obligate phototroph that is being developed for the bioproduction of high-value chemicals. Genome-scale models (GEMs) have been successfully used to assess and engineer cellular metabolism; however, GEMs of phototrophic metabolism have been limited by the lack of experimental datasets for model validation and the challenges of incorporating photon uptake. In this paper, we develop a GEM of metabolism in S. elongatus using random barcode transposon site sequencing (RB-TnSeq) essential gene and physiological data specific to photoautotrophic metabolism. The model explicitly describes photon absorption and accounts for shading, resulting inmore » the characteristic linear growth curve of photoautotrophs. GEM predictions of gene essentiality were compared with data obtained from recent dense-transposon mutagenesis experiments. This dataset allowed major improvements to the accuracy of the model. Furthermore, discrepancies between GEM predictions and the in vivo dataset revealed biological characteristics, such as the importance of a truncated, linear TCA pathway, low flux toward amino acid synthesis from photorespiration, and knowledge gaps within nucleotide metabolism. Finally, coupling of strong experimental support and photoautotrophic modeling methods thus resulted in a highly accurate model of S. elongatus metabolism that highlights previously unknown areas of S. elongatus biology.« less

Metabolic engineering approaches for production of biochemicals in food and medicinal plants.

PubMed

Wilson, Sarah A; Roberts, Susan C

2014-04-01

Historically, plants are a vital source of nutrients and pharmaceuticals. Recent advances in metabolic engineering have made it possible to not only increase the concentration of desired compounds, but also introduce novel biosynthetic pathways to a variety of species, allowing for enhanced nutritional or commercial value. To improve metabolic engineering capabilities, new transformation techniques have been developed to allow for gene specific silencing strategies or stacking of multiple genes within the same region of the chromosome. The 'omics' era has provided a new resource for elucidation of uncharacterized biosynthetic pathways, enabling novel metabolic engineering approaches. These resources are now allowing for advanced metabolic engineering of plant production systems, as well as the synthesis of increasingly complex products in engineered microbial hosts. The status of current metabolic engineering efforts is highlighted for the in vitro production of paclitaxel and the in vivo production of β-carotene in Golden Rice and other food crops. Copyright © 2014 Elsevier Ltd. All rights reserved.

Systems metabolic engineering for chemicals and materials.

PubMed

Lee, Jeong Wook; Kim, Tae Yong; Jang, Yu-Sin; Choi, Sol; Lee, Sang Yup

2011-08-01

Metabolic engineering has contributed significantly to the enhanced production of various value-added and commodity chemicals and materials from renewable resources in the past two decades. Recently, metabolic engineering has been upgraded to the systems level (thus, systems metabolic engineering) by the integrated use of global technologies of systems biology, fine design capabilities of synthetic biology, and rational-random mutagenesis through evolutionary engineering. By systems metabolic engineering, production of natural and unnatural chemicals and materials can be better optimized in a multiplexed way on a genome scale, with reduced time and effort. Here, we review the recent trends in systems metabolic engineering for the production of chemicals and materials by presenting general strategies and showcasing representative examples. Copyright © 2011 Elsevier Ltd. All rights reserved.

Systematic metabolic engineering of Methylomicrobium alcaliphilum 20Z for 2,3-butanediol production from methane.

PubMed

Nguyen, Anh Duc; Hwang, In Yeub; Lee, Ok Kyung; Kim, Donghyuk; Kalyuzhnaya, Marina G; Mariyana, Rina; Hadiyati, Susila; Kim, Min Sik; Lee, Eun Yeol

2018-04-16

Methane is considered a next-generation feedstock, and methanotrophic cell-based biorefinery is attractive for production of a variety of high-value compounds from methane. In this work, we have metabolically engineered Methylomicrobium alcaliphilum 20Z for 2,3-butanediol (2,3-BDO) production from methane. The engineered strain 20Z/pBudK.p, harboring the 2,3-BDO synthesis gene cluster (budABC) from Klebsiella pneumoniae, accumulated 2,3-BDO in methane-fed shake flask cultures with a titer of 35.66 mg/L. Expression of the most efficient gene cluster was optimized using selection of promoters, translation initiation rates (TIR), and the combination of 2,3-BDO synthesis genes from different sources. A higher 2,3-BDO titer of 57.7 mg/L was measured in the 20Z/pNBM-Re strain with budA of K. pneumoniae and budB of Bacillus subtilis under the control of the Tac promoter. The genome-scale metabolic network reconstruction of M. alcaliphilum 20Z enabled in silico gene knockout predictions using an evolutionary programming method to couple growth and 2,3-BDO production. The ldh, ack, and mdh genes in M. alcaliphilum 20Z were identified as potential knockout targets. Pursuing these targets, a triple-mutant strain ∆ldh ∆ack ∆mdh was constructed, resulting in a further increase of the 2,3-BDO titer to 68.8 mg/L. The productivity of this optimized strain was then tested in a fed-batch stirred tank bioreactor, where final product concentrations of up to 86.2 mg/L with a yield of 0.0318 g-(2,3-BDO) /g-CH 4 were obtained under O 2 -limited conditions. This study first demonstrates the strategy of in silico simulation-guided metabolic engineering and represents a proof-of-concept for the production of value-added compounds using systematic approaches from engineered methanotrophs. Copyright © 2018 International Metabolic Engineering Society. Published by Elsevier Inc. All rights reserved.

Production of L-carnitine by secondary metabolism of bacteria

PubMed Central

Bernal, Vicente; Sevilla, Ángel; Cánovas, Manuel; Iborra, José L

2007-01-01

The increasing commercial demand for L-carnitine has led to a multiplication of efforts to improve its production with bacteria. The use of different cell environments, such as growing, resting, permeabilized, dried, osmotically stressed, freely suspended and immobilized cells, to maintain enzymes sufficiently active for L-carnitine production is discussed in the text. The different cell states of enterobacteria, such as Escherichia coli and Proteus sp., which can be used to produce L-carnitine from crotonobetaine or D-carnitine as substrate, are analyzed. Moreover, the combined application of both bioprocess and metabolic engineering has allowed a deeper understanding of the main factors controlling the production process, such as energy depletion and the alteration of the acetyl-CoA/CoA ratio which are coupled to the end of the biotransformation. Furthermore, the profiles of key central metabolic activities such as the TCA cycle, the glyoxylate shunt and the acetate metabolism are seen to be closely interrelated and affect the biotransformation efficiency. Although genetically modified strains have been obtained, new strain improvement strategies are still needed, especially in Escherichia coli as a model organism for molecular biology studies. This review aims to summarize and update the state of the art in L-carnitine production using E. coli and Proteus sp, emphasizing the importance of proper reactor design and operation strategies, together with metabolic engineering aspects and the need for feed-back between wet and in silico work to optimize this biotransformation. PMID:17910757

Towards systems metabolic engineering of microorganisms for amino acid production.

PubMed

Park, Jin Hwan; Lee, Sang Yup

2008-10-01

Microorganisms capable of efficient production of amino acids have traditionally been developed by random mutation and selection method, which might cause unwanted physiological changes in cellular metabolism. Rational genome-wide metabolic engineering based on systems and synthetic biology tools, which is termed 'systems metabolic engineering', is rising as an alternative to overcome these problems. Recently, several amino acid producers have been successfully developed by systems metabolic engineering, where the metabolic engineering procedures were performed within a systems biology framework, and entire metabolic networks, including complex regulatory circuits, were engineered in an integrated manner. Here we review the current status of systems metabolic engineering successfully applied for developing amino acid producing strains and discuss future prospects.

Production of succinic acid by metabolically engineered microorganisms.

PubMed

Ahn, Jung Ho; Jang, Yu-Sin; Lee, Sang Yup

2016-12-01

Succinic acid (SA) has been recognized as one of the most important bio-based building block chemicals due to its numerous potential applications. For the economical bio-based production of SA, extensive research works have been performed on developing microbial strains by metabolic engineering as well as fermentation and downstream processes. Here we review metabolic engineering strategies applied for bio-based production of SA using representative microorganisms, including Saccharomyces cerevisiae, Pichia kudriavzevii, Escherichia coli, Mannheimia succiniciproducens, Basfia succiniciproducens, Actinobacillus succinogenes, and Corynebacterium glutamicum. In particular, strategies employed for developing engineered strains of these microorganisms leading to the best performance indices (titer, yield, and productivity) are showcased based on the published papers as well as patents. Those processes currently under commercialization are also analyzed and future perspectives are provided. Copyright © 2016 Elsevier Ltd. All rights reserved.

ReacKnock: Identifying Reaction Deletion Strategies for Microbial Strain Optimization Based on Genome-Scale Metabolic Network

PubMed Central

Xu, Zixiang; Zheng, Ping; Sun, Jibin; Ma, Yanhe

2013-01-01

Gene knockout has been used as a common strategy to improve microbial strains for producing chemicals. Several algorithms are available to predict the target reactions to be deleted. Most of them apply mixed integer bi-level linear programming (MIBLP) based on metabolic networks, and use duality theory to transform bi-level optimization problem of large-scale MIBLP to single-level programming. However, the validity of the transformation was not proved. Solution of MIBLP depends on the structure of inner problem. If the inner problem is continuous, Karush-Kuhn-Tucker (KKT) method can be used to reformulate the MIBLP to a single-level one. We adopt KKT technique in our algorithm ReacKnock to attack the intractable problem of the solution of MIBLP, demonstrated with the genome-scale metabolic network model of E. coli for producing various chemicals such as succinate, ethanol, threonine and etc. Compared to the previous methods, our algorithm is fast, stable and reliable to find the optimal solutions for all the chemical products tested, and able to provide all the alternative deletion strategies which lead to the same industrial objective. PMID:24348984

Genome-scale metabolic reconstructions and theoretical investigation of methane conversion in Methylomicrobium buryatense strain 5G(B1).

PubMed

de la Torre, Andrea; Metivier, Aisha; Chu, Frances; Laurens, Lieve M L; Beck, David A C; Pienkos, Philip T; Lidstrom, Mary E; Kalyuzhnaya, Marina G

2015-11-25

Methane-utilizing bacteria (methanotrophs) are capable of growth on methane and are attractive systems for bio-catalysis. However, the application of natural methanotrophic strains to large-scale production of value-added chemicals/biofuels requires a number of physiological and genetic alterations. An accurate metabolic model coupled with flux balance analysis can provide a solid interpretative framework for experimental data analyses and integration. A stoichiometric flux balance model of Methylomicrobium buryatense strain 5G(B1) was constructed and used for evaluating metabolic engineering strategies for biofuels and chemical production with a methanotrophic bacterium as the catalytic platform. The initial metabolic reconstruction was based on whole-genome predictions. Each metabolic step was manually verified, gapfilled, and modified in accordance with genome-wide expression data. The final model incorporates a total of 841 reactions (in 167 metabolic pathways). Of these, up to 400 reactions were recruited to produce 118 intracellular metabolites. The flux balance simulations suggest that only the transfer of electrons from methanol oxidation to methane oxidation steps can support measured growth and methane/oxygen consumption parameters, while the scenario employing NADH as a possible source of electrons for particulate methane monooxygenase cannot. Direct coupling between methane oxidation and methanol oxidation accounts for most of the membrane-associated methane monooxygenase activity. However the best fit to experimental results is achieved only after assuming that the efficiency of direct coupling depends on growth conditions and additional NADH input (about 0.1-0.2 mol of incremental NADH per one mol of methane oxidized). The additional input is proposed to cover loss of electrons through inefficiency and to sustain methane oxidation at perturbations or support uphill electron transfer. Finally, the model was used for testing the carbon conversion

Characterizing steady states of genome-scale metabolic networks in continuous cell cultures.

PubMed

Fernandez-de-Cossio-Diaz, Jorge; Leon, Kalet; Mulet, Roberto

2017-11-01

In the continuous mode of cell culture, a constant flow carrying fresh media replaces culture fluid, cells, nutrients and secreted metabolites. Here we present a model for continuous cell culture coupling intra-cellular metabolism to extracellular variables describing the state of the bioreactor, taking into account the growth capacity of the cell and the impact of toxic byproduct accumulation. We provide a method to determine the steady states of this system that is tractable for metabolic networks of arbitrary complexity. We demonstrate our approach in a toy model first, and then in a genome-scale metabolic network of the Chinese hamster ovary cell line, obtaining results that are in qualitative agreement with experimental observations. We derive a number of consequences from the model that are independent of parameter values. The ratio between cell density and dilution rate is an ideal control parameter to fix a steady state with desired metabolic properties. This conclusion is robust even in the presence of multi-stability, which is explained in our model by a negative feedback loop due to toxic byproduct accumulation. A complex landscape of steady states emerges from our simulations, including multiple metabolic switches, which also explain why cell-line and media benchmarks carried out in batch culture cannot be extrapolated to perfusion. On the other hand, we predict invariance laws between continuous cell cultures with different parameters. A practical consequence is that the chemostat is an ideal experimental model for large-scale high-density perfusion cultures, where the complex landscape of metabolic transitions is faithfully reproduced.

Characterizing steady states of genome-scale metabolic networks in continuous cell cultures

PubMed Central

Leon, Kalet; Mulet, Roberto

2017-01-01

In the continuous mode of cell culture, a constant flow carrying fresh media replaces culture fluid, cells, nutrients and secreted metabolites. Here we present a model for continuous cell culture coupling intra-cellular metabolism to extracellular variables describing the state of the bioreactor, taking into account the growth capacity of the cell and the impact of toxic byproduct accumulation. We provide a method to determine the steady states of this system that is tractable for metabolic networks of arbitrary complexity. We demonstrate our approach in a toy model first, and then in a genome-scale metabolic network of the Chinese hamster ovary cell line, obtaining results that are in qualitative agreement with experimental observations. We derive a number of consequences from the model that are independent of parameter values. The ratio between cell density and dilution rate is an ideal control parameter to fix a steady state with desired metabolic properties. This conclusion is robust even in the presence of multi-stability, which is explained in our model by a negative feedback loop due to toxic byproduct accumulation. A complex landscape of steady states emerges from our simulations, including multiple metabolic switches, which also explain why cell-line and media benchmarks carried out in batch culture cannot be extrapolated to perfusion. On the other hand, we predict invariance laws between continuous cell cultures with different parameters. A practical consequence is that the chemostat is an ideal experimental model for large-scale high-density perfusion cultures, where the complex landscape of metabolic transitions is faithfully reproduced. PMID:29131817

Large-scale transcriptome analysis reveals arabidopsis metabolic pathways are frequently influenced by different pathogens.

PubMed

Jiang, Zhenhong; He, Fei; Zhang, Ziding

2017-07-01

Through large-scale transcriptional data analyses, we highlighted the importance of plant metabolism in plant immunity and identified 26 metabolic pathways that were frequently influenced by the infection of 14 different pathogens. Reprogramming of plant metabolism is a common phenomenon in plant defense responses. Currently, a large number of transcriptional profiles of infected tissues in Arabidopsis (Arabidopsis thaliana) have been deposited in public databases, which provides a great opportunity to understand the expression patterns of metabolic pathways during plant defense responses at the systems level. Here, we performed a large-scale transcriptome analysis based on 135 previously published expression samples, including 14 different pathogens, to explore the expression pattern of Arabidopsis metabolic pathways. Overall, metabolic genes are significantly changed in expression during plant defense responses. Upregulated metabolic genes are enriched on defense responses, and downregulated genes are enriched on photosynthesis, fatty acid and lipid metabolic processes. Gene set enrichment analysis (GSEA) identifies 26 frequently differentially expressed metabolic pathways (FreDE_Paths) that are differentially expressed in more than 60% of infected samples. These pathways are involved in the generation of energy, fatty acid and lipid metabolism as well as secondary metabolite biosynthesis. Clustering analysis based on the expression levels of these 26 metabolic pathways clearly distinguishes infected and control samples, further suggesting the importance of these metabolic pathways in plant defense responses. By comparing with FreDE_Paths from abiotic stresses, we find that the expression patterns of 26 FreDE_Paths from biotic stresses are more consistent across different infected samples. By investigating the expression correlation between transcriptional factors (TFs) and FreDE_Paths, we identify several notable relationships. Collectively, the current study

Metabolic Engineering of Saccharomyces cerevisiae for High-Level Production of Salidroside from Glucose.

PubMed

Jiang, Jingjie; Yin, Hua; Wang, Shuai; Zhuang, Yibin; Liu, Shaowei; Liu, Tao; Ma, Yanhe

2018-05-02

Salidroside is an important plant-derived aromatic compound with diverse biological properties. Because of inadequate natural resources, the supply of salidroside is currently limited. In this work, we engineered the production of salidroside in yeast. First, the aromatic aldehyde synthase (AAS) from Petroselinum crispum was overexpressed in Saccharomyces cerevisiae when combined with endogenous Ehrlich pathway to produce tyrosol from tyrosine. Glucosyltransferases from different resources were tested for ideal production of salidroside in the yeast. Metabolic flux was enhanced toward tyrosine biosynthesis by overexpressing pathway genes and eliminating feedback inhibition. The pathway genes were integrated into yeast chromosome, leading to a recombinant strain that produced 239.5 mg/L salidroside and 965.4 mg/L tyrosol. The production of salidroside and tyrosol reached up to 732.5 and 1394.6 mg/L, respectively, by fed-batch fermentation. Our work provides an alternative way for industrial large-scale production of salidroside and tyrosol from S. cerevisiae.

Elucidating central metabolic redox obstacles hindering ethanol production in Clostridium thermocellum.

PubMed

Thompson, R Adam; Layton, Donovan S; Guss, Adam M; Olson, Daniel G; Lynd, Lee R; Trinh, Cong T

2015-11-01

Clostridium thermocellum is an anaerobic, Gram-positive, thermophilic bacterium that has generated great interest due to its ability to ferment lignocellulosic biomass to ethanol. However, ethanol production is low due to the complex and poorly understood branched metabolism of C. thermocellum, and in some cases overflow metabolism as well. In this work, we developed a predictive stoichiometric metabolic model for C. thermocellum which incorporates the current state of understanding, with particular attention to cofactor specificity in the atypical glycolytic enzymes and the complex energy, redox, and fermentative pathways with the goal of aiding metabolic engineering efforts. We validated the model's capability to encompass experimentally observed phenotypes for the parent strain and derived mutants designed for significant perturbation of redox and energy pathways. Metabolic flux distributions revealed significant alterations in key metabolic branch points (e.g., phosphoenol pyruvate, pyruvate, acetyl-CoA, and cofactor nodes) in engineered strains for channeling electron and carbon fluxes for enhanced ethanol synthesis, with the best performing strain doubling ethanol yield and titer compared to the parent strain. In silico predictions of a redox-imbalanced genotype incapable of growth were confirmed in vivo, and a mutant strain was used as a platform to probe redox bottlenecks in the central metabolism that hinder efficient ethanol production. The results highlight the robustness of the redox metabolism of C. thermocellum and the necessity of streamlined electron flux from reduced ferredoxin to NAD(P)H for high ethanol production. The model was further used to design a metabolic engineering strategy to phenotypically constrain C. thermocellum to achieve high ethanol yields while requiring minimal genetic manipulations. The model can be applied to design C. thermocellum as a platform microbe for consolidated bioprocessing to produce ethanol and other reduced

Pulmonary diffusional screening and the scaling laws of mammalian metabolic rates

NASA Astrophysics Data System (ADS)

Hou, Chen; Mayo, Michael

2011-12-01

Theoretical considerations suggest that the mammalian metabolic rate is linearly proportional to the surface areas of mitochondria, capillary, and alveolar membranes. However, the scaling exponents of these surface areas to the mammals' body mass (approximately 0.9-1) are higher than exponents of the resting metabolic rate (RMR) to body mass (approximately 0.75), although similar to the one of exercise metabolic rate (EMR); the underlying physiological cause of this mismatch remains unclear. The analysis presented here shows that discrepancies between the scaling exponents of RMR and the relevant surface areas may originate from, at least for the system of alveolar membranes in mammalian lungs, the facts that (i) not all of the surface area is involved in the gas exchange and (ii) that larger mammals host a smaller effective surface area that participates in the material exchange rate. A result of these facts is that lung surface areas unused at rest are activated under heavy breathing conditions (e.g., exercise), wherein larger mammals support larger activated surface areas that provide a higher capability to increase the gas-exchange rate, allowing for mammals to meet, for example, the high energetic demands of foraging and predation.

Cardiac Metabolism in Heart Failure - Implications beyond ATP production

PubMed Central

Doenst, Torsten; Nguyen, T. Dung; Abel, E. Dale

2013-01-01

The heart has a high rate of ATP production and turnover which is required to maintain its continuous mechanical work. Perturbations in ATP generating processes may therefore affect contractile function directly. Characterizing cardiac metabolism in heart failure revealed several metabolic alterations termed metabolic remodeling, ranging from changes in substrate utilization to mitochondrial dysfunction, ultimately resulting in ATP deficiency and impaired contractility. However, ATP depletion is not the only relevant consequence of metabolic remodeling during heart failure. By providing cellular building blocks and signaling molecules, metabolic pathways control essential processes such as cell growth and regeneration. Thus, alterations in cardiac metabolism may also affect the progression to heart failure by mechanisms beyond ATP supply. Our aim is therefore to highlight that metabolic remodeling in heart failure not only results in impaired cardiac energetics, but also induces other processes implicated in the development of heart failure such as structural remodeling and oxidative stress. Accordingly, modulating cardiac metabolism in heart failure may have significant therapeutic relevance that goes beyond the energetic aspect. PMID:23989714

Grohar: Automated Visualization of Genome-Scale Metabolic Models and Their Pathways.

PubMed

Moškon, Miha; Zimic, Nikolaj; Mraz, Miha

2018-05-01

Genome-scale metabolic models (GEMs) have become a powerful tool for the investigation of the entire metabolism of the organism in silico. These models are, however, often extremely hard to reconstruct and also difficult to apply to the selected problem. Visualization of the GEM allows us to easier comprehend the model, to perform its graphical analysis, to find and correct the faulty relations, to identify the parts of the system with a designated function, etc. Even though several approaches for the automatic visualization of GEMs have been proposed, metabolic maps are still manually drawn or at least require large amount of manual curation. We present Grohar, a computational tool for automatic identification and visualization of GEM (sub)networks and their metabolic fluxes. These (sub)networks can be specified directly by listing the metabolites of interest or indirectly by providing reference metabolic pathways from different sources, such as KEGG, SBML, or Matlab file. These pathways are identified within the GEM using three different pathway alignment algorithms. Grohar also supports the visualization of the model adjustments (e.g., activation or inhibition of metabolic reactions) after perturbations are induced.

Is the scaling of swim speed in sharks driven by metabolism?

PubMed Central

Jacoby, David M. P.; Siriwat, Penthai; Freeman, Robin; Carbone, Chris

2015-01-01

The movement rates of sharks are intrinsically linked to foraging ecology, predator–prey dynamics and wider ecosystem functioning in marine systems. During ram ventilation, however, shark movement rates are linked not only to ecological parameters, but also to physiology, as minimum speeds are required to provide sufficient water flow across the gills to maintain metabolism. We develop a geometric model predicting a positive scaling relationship between swim speeds in relation to body size and ultimately shark metabolism, taking into account estimates for the scaling of gill dimensions. Empirical data from 64 studies (26 species) were compiled to test our model while controlling for the influence of phylogenetic similarity between related species. Our model predictions were found to closely resemble the observed relationships from tracked sharks, providing a means to infer mobility in particularly intractable species. PMID:26631246

Multiscale Metabolic Modeling: Dynamic Flux Balance Analysis on a Whole-Plant Scale1[W][OPEN

PubMed Central

Grafahrend-Belau, Eva; Junker, Astrid; Eschenröder, André; Müller, Johannes; Schreiber, Falk; Junker, Björn H.

2013-01-01

Plant metabolism is characterized by a unique complexity on the cellular, tissue, and organ levels. On a whole-plant scale, changing source and sink relations accompanying plant development add another level of complexity to metabolism. With the aim of achieving a spatiotemporal resolution of source-sink interactions in crop plant metabolism, a multiscale metabolic modeling (MMM) approach was applied that integrates static organ-specific models with a whole-plant dynamic model. Allowing for a dynamic flux balance analysis on a whole-plant scale, the MMM approach was used to decipher the metabolic behavior of source and sink organs during the generative phase of the barley (Hordeum vulgare) plant. It reveals a sink-to-source shift of the barley stem caused by the senescence-related decrease in leaf source capacity, which is not sufficient to meet the nutrient requirements of sink organs such as the growing seed. The MMM platform represents a novel approach for the in silico analysis of metabolism on a whole-plant level, allowing for a systemic, spatiotemporally resolved understanding of metabolic processes involved in carbon partitioning, thus providing a novel tool for studying yield stability and crop improvement. PMID:23926077

Magnesium degradation products: effects on tissue and human metabolism.

PubMed

Seitz, J-M; Eifler, R; Bach, Fr-W; Maier, H J

2014-10-01

Owing to their mechanical properties, metallic materials present a promising solution in the field of resorbable implants. The magnesium metabolism in humans differs depending on its introduction. The natural, oral administration of magnesium via, for example, food, essentially leads to an intracellular enrichment of Mg(2+) . In contrast, introducing magnesium-rich substances or implants into the tissue results in a different decomposition behavior. Here, exposing magnesium to artificial body electrolytes resulted in the formation of the following products: magnesium hydroxide, magnesium oxide, and magnesium chloride, as well as calcium and magnesium apatites. Moreover, it can be assumed that Mg(2+) , OH(-) ions, and gaseous hydrogen are also present and result from the reaction for magnesium in an aqueous environment. With the aid of physiological metabolic processes, the organism succeeds in either excreting the above mentioned products or integrating them into the natural metabolic process. Only a burst release of these products is to be considered a problem. A multitude of general tissue effects and responses from the Mg's degradation products is considered within this review, which is not targeting specific implant classes. Furthermore, common alloying elements of magnesium and their hazardous potential in vivo are taken into account. © 2013 Wiley Periodicals, Inc.

Development of a commercial scale process for production of 1,4-butanediol from sugar.

PubMed

Burgard, Anthony; Burk, Mark J; Osterhout, Robin; Van Dien, Stephen; Yim, Harry

2016-12-01

A sustainable bioprocess for the production of 1,4-butanediol (BDO) from carbohydrate feedstocks was developed. BDO is a chemical intermediate that goes into a variety of products including automotive parts, electronics, and apparel, and is currently manufactured commercially through energy-intensive petrochemical processes using fossil raw materials. This review highlights the development of an Escherichia coli strain and an overall process that successfully performed at commercial scale for direct production of bio-BDO from dextrose. Achieving such high level performance required an integrated technology platform enabling detailed engineering of enzyme, pathway, metabolic network, and organism, as well as development of effective fermentation and downstream recovery processes. Copyright © 2016 Elsevier Ltd. All rights reserved.

Capturing the response of Clostridium acetobutylicum to chemical stressors using a regulated genome-scale metabolic model

DOE PAGES

Dash, Satyakam; Mueller, Thomas J.; Venkataramanan, Keerthi P.; ...

2014-10-14

Clostridia are anaerobic Gram-positive Firmicutes containing broad and flexible systems for substrate utilization, which have been used successfully to produce a range of industrial compounds. Clostridium acetobutylicum has been used to produce butanol on an industrial scale through acetone-butanol-ethanol (ABE) fermentation. A genome-scale metabolic (GSM) model is a powerful tool for understanding the metabolic capacities of an organism and developing metabolic engineering strategies for strain development. The integration of stress related specific transcriptomics information with the GSM model provides opportunities for elucidating the focal points of regulation.

Improved evidence-based genome-scale metabolic models for maize leaf, embryo, and endosperm

PubMed Central

Seaver, Samuel M. D.; Bradbury, Louis M. T.; Frelin, Océane; Zarecki, Raphy; Ruppin, Eytan; Hanson, Andrew D.; Henry, Christopher S.

2015-01-01

There is a growing demand for genome-scale metabolic reconstructions for plants, fueled by the need to understand the metabolic basis of crop yield and by progress in genome and transcriptome sequencing. Methods are also required to enable the interpretation of plant transcriptome data to study how cellular metabolic activity varies under different growth conditions or even within different organs, tissues, and developmental stages. Such methods depend extensively on the accuracy with which genes have been mapped to the biochemical reactions in the plant metabolic pathways. Errors in these mappings lead to metabolic reconstructions with an inflated number of reactions and possible generation of unreliable metabolic phenotype predictions. Here we introduce a new evidence-based genome-scale metabolic reconstruction of maize, with significant improvements in the quality of the gene-reaction associations included within our model. We also present a new approach for applying our model to predict active metabolic genes based on transcriptome data. This method includes a minimal set of reactions associated with low expression genes to enable activity of a maximum number of reactions associated with high expression genes. We apply this method to construct an organ-specific model for the maize leaf, and tissue specific models for maize embryo and endosperm cells. We validate our models using fluxomics data for the endosperm and embryo, demonstrating an improved capacity of our models to fit the available fluxomics data. All models are publicly available via the DOE Systems Biology Knowledgebase and PlantSEED, and our new method is generally applicable for analysis transcript profiles from any plant, paving the way for further in silico studies with a wide variety of plant genomes. PMID:25806041

Improved evidence-based genome-scale metabolic models for maize leaf, embryo, and endosperm

DOE PAGES

Seaver, Samuel M.D.; Bradbury, Louis M.T.; Frelin, Océane; ...

2015-03-10

There is a growing demand for genome-scale metabolic reconstructions for plants, fueled by the need to understand the metabolic basis of crop yield and by progress in genome and transcriptome sequencing. Methods are also required to enable the interpretation of plant transcriptome data to study how cellular metabolic activity varies under different growth conditions or even within different organs, tissues, and developmental stages. Such methods depend extensively on the accuracy with which genes have been mapped to the biochemical reactions in the plant metabolic pathways. Errors in these mappings lead to metabolic reconstructions with an inflated number of reactions andmore » possible generation of unreliable metabolic phenotype predictions. Here we introduce a new evidence-based genome-scale metabolic reconstruction of maize, with significant improvements in the quality of the gene-reaction associations included within our model. We also present a new approach for applying our model to predict active metabolic genes based on transcriptome data. This method includes a minimal set of reactions associated with low expression genes to enable activity of a maximum number of reactions associated with high expression genes. We apply this method to construct an organ-specific model for the maize leaf, and tissue specific models for maize embryo and endosperm cells. We validate our models using fluxomics data for the endosperm and embryo, demonstrating an improved capacity of our models to fit the available fluxomics data. All models are publicly available via the DOE Systems Biology Knowledgebase and PlantSEED, and our new method is generally applicable for analysis transcript profiles from any plant, paving the way for further in silico studies with a wide variety of plant genomes.« less

Improved triacylglycerol production in Acinetobacter baylyi ADP1 by metabolic engineering.

PubMed

Santala, Suvi; Efimova, Elena; Kivinen, Virpi; Larjo, Antti; Aho, Tommi; Karp, Matti; Santala, Ville

2011-05-18

Triacylglycerols are used in various purposes including food applications, cosmetics, oleochemicals and biofuels. Currently the main sources for triacylglycerol are vegetable oils, and microbial triacylglycerol has been suggested as an alternative for these. Due to the low production rates and yields of microbial processes, the role of metabolic engineering has become more significant. As a robust model organism for genetic and metabolic studies, and for the natural capability to produce triacylglycerol, Acinetobacter baylyi ADP1 serves as an excellent organism for modelling the effects of metabolic engineering for energy molecule biosynthesis. Beneficial gene deletions regarding triacylglycerol production were screened by computational means exploiting the metabolic model of ADP1. Four deletions, acr1, poxB, dgkA, and a triacylglycerol lipase were chosen to be studied experimentally both separately and concurrently by constructing a knock-out strain (MT) with three of the deletions. Improvements in triacylglycerol production were observed: the strain MT produced 5.6 fold more triacylglycerol (mg/g cell dry weight) compared to the wild type strain, and the proportion of triacylglycerol in total lipids was increased by 8-fold. In silico predictions of beneficial gene deletions were verified experimentally. The chosen single and multiple gene deletions affected beneficially the natural triacylglycerol metabolism of A. baylyi ADP1. This study demonstrates the importance of single gene deletions in triacylglycerol metabolism, and proposes Acinetobacter sp. ADP1 as a model system for bioenergetic studies regarding metabolic engineering.

Production of bulk chemicals via novel metabolic pathways in microorganisms.

PubMed

Shin, Jae Ho; Kim, Hyun Uk; Kim, Dong In; Lee, Sang Yup

2013-11-01

Metabolic engineering has been playing important roles in developing high performance microorganisms capable of producing various chemicals and materials from renewable biomass in a sustainable manner. Synthetic and systems biology are also contributing significantly to the creation of novel pathways and the whole cell-wide optimization of metabolic performance, respectively. In order to expand the spectrum of chemicals that can be produced biotechnologically, it is necessary to broaden the metabolic capacities of microorganisms. Expanding the metabolic pathways for biosynthesizing the target chemicals requires not only the enumeration of a series of known enzymes, but also the identification of biochemical gaps whose corresponding enzymes might not actually exist in nature; this issue is the focus of this paper. First, pathway prediction tools, effectively combining reactions that lead to the production of a target chemical, are analyzed in terms of logics representing chemical information, and designing and ranking the proposed metabolic pathways. Then, several approaches for potentially filling in the gaps of the novel metabolic pathway are suggested along with relevant examples, including the use of promiscuous enzymes that flexibly utilize different substrates, design of novel enzymes for non-natural reactions, and exploration of hypothetical proteins. Finally, strain optimization by systems metabolic engineering in the context of novel metabolic pathways constructed is briefly described. It is hoped that this review paper will provide logical ways of efficiently utilizing 'big' biological data to design and develop novel metabolic pathways for the production of various bulk chemicals that are currently produced from fossil resources. Copyright © 2012 Elsevier Inc. All rights reserved.

Exploring metabolic pathways in genome-scale networks via generating flux modes.

PubMed

Rezola, A; de Figueiredo, L F; Brock, M; Pey, J; Podhorski, A; Wittmann, C; Schuster, S; Bockmayr, A; Planes, F J

2011-02-15

The reconstruction of metabolic networks at the genome scale has allowed the analysis of metabolic pathways at an unprecedented level of complexity. Elementary flux modes (EFMs) are an appropriate concept for such analysis. However, their number grows in a combinatorial fashion as the size of the metabolic network increases, which renders the application of EFMs approach to large metabolic networks difficult. Novel methods are expected to deal with such complexity. In this article, we present a novel optimization-based method for determining a minimal generating set of EFMs, i.e. a convex basis. We show that a subset of elements of this convex basis can be effectively computed even in large metabolic networks. Our method was applied to examine the structure of pathways producing lysine in Escherichia coli. We obtained a more varied and informative set of pathways in comparison with existing methods. In addition, an alternative pathway to produce lysine was identified using a detour via propionyl-CoA, which shows the predictive power of our novel approach. The source code in C++ is available upon request.

Network Thermodynamic Curation of Human and Yeast Genome-Scale Metabolic Models

PubMed Central

Martínez, Verónica S.; Quek, Lake-Ee; Nielsen, Lars K.

2014-01-01

Genome-scale models are used for an ever-widening range of applications. Although there has been much focus on specifying the stoichiometric matrix, the predictive power of genome-scale models equally depends on reaction directions. Two-thirds of reactions in the two eukaryotic reconstructions Homo sapiens Recon 1 and Yeast 5 are specified as irreversible. However, these specifications are mainly based on biochemical textbooks or on their similarity to other organisms and are rarely underpinned by detailed thermodynamic analysis. In this study, a to our knowledge new workflow combining network-embedded thermodynamic and flux variability analysis was used to evaluate existing irreversibility constraints in Recon 1 and Yeast 5 and to identify new ones. A total of 27 and 16 new irreversible reactions were identified in Recon 1 and Yeast 5, respectively, whereas only four reactions were found with directions incorrectly specified against thermodynamics (three in Yeast 5 and one in Recon 1). The workflow further identified for both models several isolated internal loops that require further curation. The framework also highlighted the need for substrate channeling (in human) and ATP hydrolysis (in yeast) for the essential reaction catalyzed by phosphoribosylaminoimidazole carboxylase in purine metabolism. Finally, the framework highlighted differences in proline metabolism between yeast (cytosolic anabolism and mitochondrial catabolism) and humans (exclusively mitochondrial metabolism). We conclude that network-embedded thermodynamics facilitates the specification and validation of irreversibility constraints in compartmentalized metabolic models, at the same time providing further insight into network properties. PMID:25028891

Metabolic engineering of yeast for lignocellulosic biofuel production.

PubMed

Jin, Yong-Su; Cate, Jamie Hd

2017-12-01

Production of biofuels from lignocellulosic biomass remains an unsolved challenge in industrial biotechnology. Efforts to use yeast for conversion face the question of which host organism to use, counterbalancing the ease of genetic manipulation with the promise of robust industrial phenotypes. Saccharomyces cerevisiae remains the premier host for metabolic engineering of biofuel pathways, due to its many genetic, systems and synthetic biology tools. Numerous engineering strategies for expanding substrate ranges and diversifying products of S. cerevisiae have been developed. Other yeasts generally lack these tools, yet harbor superior phenotypes that could be exploited in the harsh processes required for lignocellulosic biofuel production. These include thermotolerance, resistance to toxic compounds generated during plant biomass deconstruction, and wider carbon consumption capabilities. Although promising, these yeasts have yet to be widely exploited. By contrast, oleaginous yeasts such as Yarrowia lipolytica capable of producing high titers of lipids are rapidly advancing in terms of the tools available for their metabolic manipulation. Copyright © 2017 Elsevier Ltd. All rights reserved.

Elucidating central metabolic redox obstacles hindering ethanol production in Clostridium thermocellum

DOE PAGES

Thompson, R. Adam; Layton, Donovan S.; Guss, Adam M.; ...

2015-10-21

Clostridium thermocellum is an anaerobic, Gram-positive, thermophilic bacterium that has generated great interest due to its ability to ferment lignocellulosic biomass to ethanol. However, ethanol production is low due to the complex and poorly understood branched metabolism of C. thermocellum, and in some cases overflow metabolism as well. In this work, we developed a predictive stoichiometric metabolic model for C. thermocellum which incorporates the current state of understanding, with particular attention to cofactor specificity in the atypical glycolytic enzymes and the complex energy, redox, and fermentative pathways with the goal of aiding metabolic engineering efforts. We validated the model smore » capability to encompass experimentally observed phenotypes for the parent strain and derived mutants designed for significant perturbation of redox and energy pathways. Metabolic flux distributions revealed significant alterations in key metabolic branch points (e.g., phosphoenol pyruvate, pyruvate, acetyl-CoA, and cofactor nodes) in engineered strains for channeling electron and carbon fluxes for enhanced ethanol synthesis, with the best performing strain doubling ethanol yield and titer compared to the parent strain. In silico predictions of a redox-imbalanced genotype incapable of growth were confirmed in vivo, and a mutant strain was used as a platform to probe redox bottlenecks in the central metabolism that hinder efficient ethanol production. The results highlight the robustness of the redox metabolism of C. thermocellum and the necessity of streamlined electron flux from reduced ferredoxin to NAD(P)H for high ethanol production. The model was further used to design a metabolic engineering strategy to phenotypically constrain C. thermocellum to achieve high ethanol yields while requiring minimal genetic manipulations. Furthermore, the model can be applied to design C. thermocellum as a platform microbe for consolidated bioprocessing to produce ethanol

Integration and Validation of the Genome-Scale Metabolic Models of Pichia pastoris: A Comprehensive Update of Protein Glycosylation Pathways, Lipid and Energy Metabolism.

PubMed

Tomàs-Gamisans, Màrius; Ferrer, Pau; Albiol, Joan

2016-01-01

Genome-scale metabolic models (GEMs) are tools that allow predicting a phenotype from a genotype under certain environmental conditions. GEMs have been developed in the last ten years for a broad range of organisms, and are used for multiple purposes such as discovering new properties of metabolic networks, predicting new targets for metabolic engineering, as well as optimizing the cultivation conditions for biochemicals or recombinant protein production. Pichia pastoris is one of the most widely used organisms for heterologous protein expression. There are different GEMs for this methylotrophic yeast of which the most relevant and complete in the published literature are iPP668, PpaMBEL1254 and iLC915. However, these three models differ regarding certain pathways, terminology for metabolites and reactions and annotations. Moreover, GEMs for some species are typically built based on the reconstructed models of related model organisms. In these cases, some organism-specific pathways could be missing or misrepresented. In order to provide an updated and more comprehensive GEM for P. pastoris, we have reconstructed and validated a consensus model integrating and merging all three existing models. In this step a comprehensive review and integration of the metabolic pathways included in each one of these three versions was performed. In addition, the resulting iMT1026 model includes a new description of some metabolic processes. Particularly new information described in recently published literature is included, mainly related to fatty acid and sphingolipid metabolism, glycosylation and cell energetics. Finally the reconstructed model was tested and validated, by comparing the results of the simulations with available empirical physiological datasets results obtained from a wide range of experimental conditions, such as different carbon sources, distinct oxygen availability conditions, as well as producing of two different recombinant proteins. In these simulations, the

Intraspecific scaling in frog calls: the interplay of temperature, body size and metabolic condition.

PubMed

Ziegler, Lucia; Arim, Matías; Bozinovic, Francisco

2016-07-01

Understanding physiological and environmental determinants of strategies of reproductive allocation is a pivotal aim in biology. Because of their high metabolic cost, properties of sexual acoustic signals may correlate with body size, temperature, and an individual's energetic state. A quantitative theory of acoustic communication, based on the metabolic scaling with temperature and mass, was recently proposed, adding to the well-reported empirical patterns. It provides quantitative predictions for frequencies, call rate, and durations. Here, we analysed the mass, temperature, and body condition scaling of spectral and temporal attributes of the advertisement call of the treefrog Hypsiboas pulchellus. Mass dependence of call frequency followed metabolic expectations (f~M (-0.25), where f is frequency and M is mass) although non-metabolic allometry could also account for the observed pattern. Temporal variables scaled inversely with mass contradicting metabolic expectations (d~M (0.25), where d is duration), supporting instead empirical patterns reported to date. Temperature was positively associated with call rate and negatively with temporal variables, which is congruent with metabolic predictions. We found no significant association between temperature and frequencies, adding to the bulk of empirical evidence. Finally, a result of particular relevance was that body condition consistently determined call characteristics, in interaction with temperature or mass. Our intraspecific study highlights that even if proximate determinants of call variability are rather well understood, the mechanisms through which they operate are proving to be more complex than previously thought. The determinants of call characteristics emerge as a key topic of research in behavioural and physiological biology, with several clear points under debate which need to be analysed on theoretical and empirical grounds.

Improved eicosapentaenoic acid production in Pythium splendens RBB-5 based on metabolic regulation analysis.

PubMed

Ren, Liang; Zhou, Pengpeng; Zhu, Yuanmin; Zhang, Ruijiao; Yu, Longjiang

2017-05-01

Eicosapentaenoic acid (EPA) is an essential polyunsaturated fatty acid for human beings. At present, the production of commercially available long-chain polyunsaturated fatty acids, mainly from wild-caught ocean fish, is struggling to meet the increasing demand for EPA. Production of EPA by microorganisms may be an alternative, effective and economical method. The oleaginous fungus Pythium splendens RBB-5 is a potential source of EPA, and thanks to the simple culture conditions required, high yields can be achieved in a facile manner. In the study, lipid metabolomics was performed in an attempt to enhance EPA biosynthesis in Pythium splendens. Synthetic, metabolic regulation and gene expression analyses were conducted to clarify the mechanism of EPA biosynthesis, and guide optimization of EPA production. The results showed that the Δ 6 desaturase pathway is the main EPA biosynthetic route in this organism, and ∆ 6 , ∆ 12 and Δ 17 desaturases are the rate-limiting enzymes. All the three desaturase genes were separately introduced into the parent strain to increase the flow of fatty acids into the Δ 6 desaturase pathway. Enhanced expression of these key enzymes, in combination with improved regulation of metabolism, resulted in a maximum yield of 1.43 g/L in the D12 transgenic strain, which represents a tenfold increase over the parent strain before optimization. This is the higher EPA production yield yet reported for a microbial system. Our findings may allow the production of EPA at an industrial scale, and the strategy employed could be used to increase the production of EPA or other lipids in oleaginous microorganisms.

Adjuvant activity of peptidoglycan monomer and its metabolic products.

PubMed

Halassy, Beata; Krstanović, Marina; Frkanec, Ruza; Tomasić, Jelka

2003-02-14

Peptidoglycan monomer (PGM) is a natural compound of bacterial origin. It is a non-toxic, non-pyrogenic, water-soluble immunostimulator potentiating humoral immune response to ovalbumin (OVA) in mice. It is fast degraded and its metabolic products-the pentapeptide (PP) and the disaccharide (DS)-are excreted from the mammalian organism upon parenteral administration. The present study investigates: (a). whether PGM could influence the long-living memory generation; (b). whether metabolic products retain adjuvant properties of the parent compound and contribute to its adjuvanticity. We report now that mice immunised twice with OVA+PGM had significantly higher anti-OVA IgG levels upon challenge with antigen alone 6 months later in comparison to control group immunised with OVA only. PP and DS were prepared enzymatically in vitro as apyrogenic and chemically pure compounds. When mice were immunised with OVA plus PP and DS, respectively, the level of anti-OVA IgGs in sera was not higher than in mice immunised with OVA alone, while PGM raised the level of specific antibodies. Results implicate that the adjuvant active molecule, capable of enhancing long-living memory generation, is PGM itself, and none of its metabolic products.

Systems metabolic engineering design: Fatty acid production as an emerging case study

PubMed Central

Tee, Ting Wei; Chowdhury, Anupam; Maranas, Costas D; Shanks, Jacqueline V

2014-01-01

Increasing demand for petroleum has stimulated industry to develop sustainable production of chemicals and biofuels using microbial cell factories. Fatty acids of chain lengths from C6 to C16 are propitious intermediates for the catalytic synthesis of industrial chemicals and diesel-like biofuels. The abundance of genetic information available for Escherichia coli and specifically, fatty acid metabolism in E. coli, supports this bacterium as a promising host for engineering a biocatalyst for the microbial production of fatty acids. Recent successes rooted in different features of systems metabolic engineering in the strain design of high-yielding medium chain fatty acid producing E. coli strains provide an emerging case study of design methods for effective strain design. Classical metabolic engineering and synthetic biology approaches enabled different and distinct design paths towards a high-yielding strain. Here we highlight a rational strain design process in systems biology, an integrated computational and experimental approach for carboxylic acid production, as an alternative method. Additional challenges inherent in achieving an optimal strain for commercialization of medium chain-length fatty acids will likely require a collection of strategies from systems metabolic engineering. Not only will the continued advancement in systems metabolic engineering result in these highly productive strains more quickly, this knowledge will extend more rapidly the carboxylic acid platform to the microbial production of carboxylic acids with alternate chain-lengths and functionalities. PMID:24481660

Integrating Kinetic Model of E. coli with Genome Scale Metabolic Fluxes Overcomes Its Open System Problem and Reveals Bistability in Central Metabolism

PubMed Central

Mannan, Ahmad A.; Toya, Yoshihiro; Shimizu, Kazuyuki; McFadden, Johnjoe; Kierzek, Andrzej M.; Rocco, Andrea

2015-01-01

An understanding of the dynamics of the metabolic profile of a bacterial cell is sought from a dynamical systems analysis of kinetic models. This modelling formalism relies on a deterministic mathematical description of enzyme kinetics and their metabolite regulation. However, it is severely impeded by the lack of available kinetic information, limiting the size of the system that can be modelled. Furthermore, the subsystem of the metabolic network whose dynamics can be modelled is faced with three problems: how to parameterize the model with mostly incomplete steady state data, how to close what is now an inherently open system, and how to account for the impact on growth. In this study we address these challenges of kinetic modelling by capitalizing on multi-‘omics’ steady state data and a genome-scale metabolic network model. We use these to generate parameters that integrate knowledge embedded in the genome-scale metabolic network model, into the most comprehensive kinetic model of the central carbon metabolism of E. coli realized to date. As an application, we performed a dynamical systems analysis of the resulting enriched model. This revealed bistability of the central carbon metabolism and thus its potential to express two distinct metabolic states. Furthermore, since our model-informing technique ensures both stable states are constrained by the same thermodynamically feasible steady state growth rate, the ensuing bistability represents a temporal coexistence of the two states, and by extension, reveals the emergence of a phenotypically heterogeneous population. PMID:26469081

Metabolic engineering for improved production of ethanol by Corynebacterium glutamicum.

PubMed

Jojima, Toru; Noburyu, Ryoji; Sasaki, Miho; Tajima, Takahisa; Suda, Masako; Yukawa, Hideaki; Inui, Masayuki

2015-02-01

Recombinant Corynebacterium glutamicum harboring genes for pyruvate decarboxylase (pdc) and alcohol dehydrogenase (adhB) can produce ethanol under oxygen deprivation. We investigated the effects of elevating the expression levels of glycolytic genes, as well as pdc and adhB, on ethanol production. Overexpression of four glycolytic genes (pgi, pfkA, gapA, and pyk) in C. glutamicum significantly increased the rate of ethanol production. Overexpression of tpi, encoding triosephosphate isomerase, further enhanced productivity. Elevated expression of pdc and adhB increased ethanol yield, but not the rate of production. Fed-batch fermentation using an optimized strain resulted in ethanol production of 119 g/L from 245 g/L glucose with a yield of 95% of the theoretical maximum. Further metabolic engineering, including integration of the genes for xylose and arabinose metabolism, enabled consumption of glucose, xylose, and arabinose, and ethanol production (83 g/L) at a yield of 90 %. This study demonstrated that C. glutamicum has significant potential for the production of cellulosic ethanol.

Metabolic engineering of Corynebacterium glutamicum for fermentative production of chemicals in biorefinery.

PubMed

Baritugo, Kei-Anne; Kim, Hee Taek; David, Yokimiko; Choi, Jong-Il; Hong, Soon Ho; Jeong, Ki Jun; Choi, Jong Hyun; Joo, Jeong Chan; Park, Si Jae

2018-05-01

Bio-based production of industrially important chemicals provides an eco-friendly alternative to current petrochemical-based processes. Because of the limited supply of fossil fuel reserves, various technologies utilizing microbial host strains for the sustainable production of platform chemicals from renewable biomass have been developed. Corynebacterium glutamicum is a non-pathogenic industrial microbial species traditionally used for L-glutamate and L-lysine production. It is a promising species for industrial production of bio-based chemicals because of its flexible metabolism that allows the utilization of a broad spectrum of carbon sources and the production of various amino acids. Classical breeding, systems, synthetic biology, and metabolic engineering approaches have been used to improve its applications, ranging from traditional amino-acid production to modern biorefinery systems for production of value-added platform chemicals. This review describes recent advances in the development of genetic engineering tools and techniques for the establishment and optimization of metabolic pathways for bio-based production of major C2-C6 platform chemicals using recombinant C. glutamicum.

Systems biology analysis of drivers underlying hallmarks of cancer cell metabolism

NASA Astrophysics Data System (ADS)

Zielinski, Daniel C.; Jamshidi, Neema; Corbett, Austin J.; Bordbar, Aarash; Thomas, Alex; Palsson, Bernhard O.

2017-01-01

Malignant transformation is often accompanied by significant metabolic changes. To identify drivers underlying these changes, we calculated metabolic flux states for the NCI60 cell line collection and correlated the variance between metabolic states of these lines with their other properties. The analysis revealed a remarkably consistent structure underlying high flux metabolism. The three primary uptake pathways, glucose, glutamine and serine, are each characterized by three features: (1) metabolite uptake sufficient for the stoichiometric requirement to sustain observed growth, (2) overflow metabolism, which scales with excess nutrient uptake over the basal growth requirement, and (3) redox production, which also scales with nutrient uptake but greatly exceeds the requirement for growth. We discovered that resistance to chemotherapeutic drugs in these lines broadly correlates with the amount of glucose uptake. These results support an interpretation of the Warburg effect and glutamine addiction as features of a growth state that provides resistance to metabolic stress through excess redox and energy production. Furthermore, overflow metabolism observed may indicate that mitochondrial catabolic capacity is a key constraint setting an upper limit on the rate of cofactor production possible. These results provide a greater context within which the metabolic alterations in cancer can be understood.

Natural Products to Counteract the Epidemic of Cardiovascular and Metabolic Disorders

PubMed Central

Šmejkal, Karel; Heiss, Elke H.; Atanasov, Atanas G.

2016-01-01

Natural products have always been exploited to promote health and served as a valuable source for the discovery of new drugs. In this review, the great potential of natural compounds and medicinal plants for the treatment or prevention of cardiovascular and metabolic disorders, global health problems with rising prevalence, is addressed. Special emphasis is laid on natural products for which efficacy and safety have already been proven and which are in clinical trials, as well as on plants used in traditional medicine. Potential benefits from certain dietary habits and dietary constituents, as well as common molecular targets of natural products, are also briefly discussed. A glimpse at the history of statins and biguanides, two prominent representatives of natural products (or their derivatives) in the fight against metabolic disease, is also included. The present review aims to serve as an “opening” of this special issue of Molecules, presenting key historical developments, recent advances, and future perspectives outlining the potential of natural products for prevention or therapy of cardiovascular and metabolic disease. PMID:27338339

Natural Products to Counteract the Epidemic of Cardiovascular and Metabolic Disorders.

PubMed

Waltenberger, Birgit; Mocan, Andrei; Šmejkal, Karel; Heiss, Elke H; Atanasov, Atanas G

2016-06-22

Natural products have always been exploited to promote health and served as a valuable source for the discovery of new drugs. In this review, the great potential of natural compounds and medicinal plants for the treatment or prevention of cardiovascular and metabolic disorders, global health problems with rising prevalence, is addressed. Special emphasis is laid on natural products for which efficacy and safety have already been proven and which are in clinical trials, as well as on plants used in traditional medicine. Potential benefits from certain dietary habits and dietary constituents, as well as common molecular targets of natural products, are also briefly discussed. A glimpse at the history of statins and biguanides, two prominent representatives of natural products (or their derivatives) in the fight against metabolic disease, is also included. The present review aims to serve as an "opening" of this special issue of Molecules, presenting key historical developments, recent advances, and future perspectives outlining the potential of natural products for prevention or therapy of cardiovascular and metabolic disease.

An Experimentally-Supported Genome-Scale Metabolic Network Reconstruction for Yersinia pestis CO92

DOE Office of Scientific and Technical Information (OSTI.GOV)

Charusanti, Pep; Chauhan, Sadhana; Mcateer, Kathleen

2011-10-13

Yersinia pestis is a gram-negative bacterium that causes plague, a disease linked historically to the Black Death in Europe during the Middle Ages and to several outbreaks during the modern era. Metabolism in Y. pestis displays remarkable flexibility and robustness, allowing the bacterium to proliferate in both warm-blooded mammalian hosts and cold-blooded insect vectors such as fleas. Here we report a genome-scale reconstruction and mathematical model of metabolism for Y. pestis CO92 and supporting experimental growth and metabolite measurements. The model contains 815 genes, 678 proteins, 963 unique metabolites and 1678 reactions, accurately simulates growth on a range of carbonmore » sources both qualitatively and quantitatively, and identifies gaps in several key biosynthetic pathways and suggests how those gaps might be filled. Furthermore, our model presents hypotheses to explain certain known nutritional requirements characteristic of this strain. Y. pestis continues to be a dangerous threat to human health during modern times. The Y. pestis genome-scale metabolic reconstruction presented here, which has been benchmarked against experimental data and correctly reproduces known phenotypes, thus provides an in silico platform with which to investigate the metabolism of this important human pathogen.« less

Metabolic engineering of Yarrowia lipolytica for industrial applications.

PubMed

Zhu, Quinn; Jackson, Ethel N

2015-12-01

Yarrowia lipolytica is a safe and robust yeast that has a history of industrial applications. Its physiological, metabolic and genomic characteristics have made it a superior host for metabolic engineering. The results of optimizing internal pathways and introducing new pathways have demonstrated that Y. lipolytica can be a platform cell factory for cost-effective production of chemicals and fuels derived from fatty acids, lipids and acetyl-CoA. Two products have been commercialized from metabolically engineered Y. lipolytica strains producing high amounts of omega-3 eicosapentaenoic acid, and more products are on the way to be produced at industrial scale. Here we review recent progress in metabolic engineering of Y. lipolytica for production of biodiesel fuel, functional fatty acids and carotenoids. Copyright © 2015 The Authors. Published by Elsevier Ltd.. All rights reserved.

A refined genome-scale reconstruction of Chlamydomonas metabolism provides a platform for systems-level analyses

DOE PAGES

Imam, Saheed; Schäuble, Sascha; Valenzuela, Jacob; ...

2015-10-20

Microalgae have reemerged as organisms of prime biotechnological interest due to their ability to synthesize a suite of valuable chemicals. To harness the capabilities of these organisms, we need a comprehensive systems-level understanding of their metabolism, which can be fundamentally achieved through large-scale mechanistic models of metabolism. In this study, we present a revised and significantly improved genome-scale metabolic model for the widely-studied microalga, Chlamydomonas reinhardtii. The model, iCre1355, represents a major advance over previous models, both in content and predictive power. iCre1355 encompasses a broad range of metabolic functions encoded across the nuclear, chloroplast and mitochondrial genomes accounting formore » 1355 genes (1460 transcripts), 2394 and 1133 metabolites. We found improved performance over the previous metabolic model based on comparisons of predictive accuracy across 306 phenotypes (from 81 mutants), lipid yield analysis and growth rates derived from chemostat-grown cells (under three conditions). Measurement of macronutrient uptake revealed carbon and phosphate to be good predictors of growth rate, while nitrogen consumption appeared to be in excess. We analyzed high-resolution time series transcriptomics data using iCre1355 to uncover dynamic pathway-level changes that occur in response to nitrogen starvation and changes in light intensity. This approach enabled accurate prediction of growth rates, the cessation of growth and accumulation of triacylglycerols during nitrogen starvation, and the temporal response of different growth-associated pathways to increased light intensity. Thus, iCre1355 represents an experimentally validated genome-scale reconstruction of C. reinhardtii metabolism that should serve as a useful resource for studying the metabolic processes of this and related microalgae.« less

Exploring Hydrogenotrophic Methanogenesis: a Genome Scale Metabolic Reconstruction of Methanococcus maripaludis

DOE PAGES

Richards, Matthew A.; Lie, Thomas J.; Zhang, Juan; ...

2016-10-10

Hydrogenotrophic methanogenesis occurs in multiple environments, ranging from the intestinal tracts of animals to anaerobic sediments and hot springs. Energy conservation in hydrogenotrophic methanogens was long a mystery; only within the last decade was it reported that net energy conservation for growth depends on electron bifurcation. In this work, we focus onMethanococcus maripaludis, a well-studied hydrogenotrophic marine methanogen. To better understand hydrogenotrophic methanogenesis and compare it with methylotrophic methanogenesis that utilizes oxidative phosphorylation rather than electron bifurcation, we have built iMR539, a genome scale metabolic reconstruction that accounts for 539 of the 1,722 protein-coding genes ofM. maripaludisstrain S2. Our reconstructedmore » metabolic network uses recent literature to not only represent the central electron bifurcation reaction but also incorporate vital biosynthesis and assimilation pathways, including unique cofactor and coenzyme syntheses. We show that our model accurately predicts experimental growth and gene knockout data, with 93% accuracy and a Matthews correlation coefficient of 0.78. Furthermore, we use our metabolic network reconstruction to probe the implications of electron bifurcation by showing its essentiality, as well as investigating the infeasibility of aceticlastic methanogenesis in the network. Additionally, we demonstrate a method of applying thermodynamic constraints to a metabolic model to quickly estimate overall free-energy changes between what comes in and out of the cell. Finally, we describe a novel reconstruction-specific computational toolbox we created to improve usability. Together, our results provide a computational network for exploring hydrogenotrophic methanogenesis and confirm the importance of electron bifurcation in this process. Understanding and applying hydrogenotrophic methanogenesis is a promising avenue for developing new bioenergy technologies around methane gas

Scaling of number, size, and metabolic rate of cells with body size in mammals.

PubMed

Savage, Van M; Allen, Andrew P; Brown, James H; Gillooly, James F; Herman, Alexander B; Woodruff, William H; West, Geoffrey B

2007-03-13

The size and metabolic rate of cells affect processes from the molecular to the organismal level. We present a quantitative, theoretical framework for studying relationships among cell volume, cellular metabolic rate, body size, and whole-organism metabolic rate that helps reveal the feedback between these levels of organization. We use this framework to show that average cell volume and average cellular metabolic rate cannot both remain constant with changes in body size because of the well known body-size dependence of whole-organism metabolic rate. Based on empirical data compiled for 18 cell types in mammals, we find that many cell types, including erythrocytes, hepatocytes, fibroblasts, and epithelial cells, follow a strategy in which cellular metabolic rate is body size dependent and cell volume is body size invariant. We suggest that this scaling holds for all quickly dividing cells, and conversely, that slowly dividing cells are expected to follow a strategy in which cell volume is body size dependent and cellular metabolic rate is roughly invariant with body size. Data for slowly dividing neurons and adipocytes show that cell volume does indeed scale with body size. From these results, we argue that the particular strategy followed depends on the structural and functional properties of the cell type. We also discuss consequences of these two strategies for cell number and capillary densities. Our results and conceptual framework emphasize fundamental constraints that link the structure and function of cells to that of whole organisms.

Large-scale variation in subsurface stream biofilms: a cross-regional comparison of metabolic function and community similarity.

PubMed

Findlay, S; Sinsabaugh, R L

2006-10-01

We examined bacterial metabolic activity and community similarity in shallow subsurface stream sediments distributed across three regions of the eastern United States to assess whether there were parallel changes in functional and structural attributes at this large scale. Bacterial growth, oxygen consumption, and a suite of extracellular enzyme activities were assayed to describe functional variability. Community similarity was assessed using randomly amplified polymorphic DNA (RAPD) patterns. There were significant differences in streamwater chemistry, metabolic activity, and bacterial growth among regions with, for instance, twofold higher bacterial production in streams near Baltimore, MD, compared to Hubbard Brook, NH. Five of eight extracellular enzymes showed significant differences among regions. Cluster analyses of individual streams by metabolic variables showed clear groups with significant differences in representation of sites from different regions among groups. Clustering of sites based on randomly amplified polymorphic DNA banding resulted in groups with generally less internal similarity although there were still differences in distribution of regional sites. There was a marginally significant (p = 0.09) association between patterns based on functional and structural variables. There were statistically significant but weak (r2 approximately 30%) associations between landcover and measures of both structure and function. These patterns imply a large-scale organization of biofilm communities and this structure may be imposed by factor(s) such as landcover and covariates such as nutrient concentrations, which are known to also cause differences in macrobiota of stream ecosystems.

Metabolic survey of Botryococcus braunii: Impact of the physiological state on product formation.

PubMed

Blifernez-Klassen, Olga; Chaudhari, Swapnil; Klassen, Viktor; Wördenweber, Robin; Steffens, Tim; Cholewa, Dominik; Niehaus, Karsten; Kalinowski, Jörn; Kruse, Olaf

2018-01-01

The microalga Botryococcus braunii is widely regarded as a potential renewable and sustainable source for industrial applications because of its capability to produce large amounts of metabolically expensive (exo-) polysaccharides and lipids, notably hydrocarbons. A comprehensive and systematic metabolic characterization of the Botryococcus braunii race A strain CCAP 807/2 was conducted within the present study, including the detailed analysis of growth-associated and physiological parameters. In addition, the intracellular metabolome was profiled for the first time and showed growth- and product-specific fluctuations in response to the different availability of medium resources during the cultivation course. Among the identified metabolites, a constant expression of raffinose was observed for the first time under standard conditions, which has until now only been described for higher plants. Overall, the multilayered analysis during the cultivation of strain CCAP 807/2 allowed the differentiation of four distinct physiological growth phases and revealed differences in the production profiles and content of liquid hydrocarbons and carbohydrates with up to 84% of organic dry weight (oDW). In the process, an enhanced production of carbohydrates with up to 63% of oDW (1.36±0.03 g L-1) could be observed during the late linear growth phase, whereas the highest accumulation of extracellular hydrocarbons with up to 24% of oDW (0.66±0.12 g L-1) occurred mainly during the stationary growth phase. Altogether, the knowledge obtained is potentially useful for the general understanding of the overall physiology of Botryococcus braunii and provide important insights into the growth behavior and product formation of this microalga, and is thus relevant for large scale biofuel production and industrial applications.

Plant interactions alter the predictions of metabolic scaling theory.

PubMed

Lin, Yue; Berger, Uta; Grimm, Volker; Huth, Franka; Weiner, Jacob

2013-01-01

Metabolic scaling theory (MST) is an attempt to link physiological processes of individual organisms with macroecology. It predicts a power law relationship with an exponent of -4/3 between mean individual biomass and density during density-dependent mortality (self-thinning). Empirical tests have produced variable results, and the validity of MST is intensely debated. MST focuses on organisms' internal physiological mechanisms but we hypothesize that ecological interactions can be more important in determining plant mass-density relationships induced by density. We employ an individual-based model of plant stand development that includes three elements: a model of individual plant growth based on MST, different modes of local competition (size-symmetric vs. -asymmetric), and different resource levels. Our model is consistent with the observed variation in the slopes of self-thinning trajectories. Slopes were significantly shallower than -4/3 if competition was size-symmetric. We conclude that when the size of survivors is influenced by strong ecological interactions, these can override predictions of MST, whereas when surviving plants are less affected by interactions, individual-level metabolic processes can scale up to the population level. MST, like thermodynamics or biomechanics, sets limits within which organisms can live and function, but there may be stronger limits determined by ecological interactions. In such cases MST will not be predictive.

Randomizing Genome-Scale Metabolic Networks

PubMed Central

Samal, Areejit; Martin, Olivier C.

2011-01-01

Networks coming from protein-protein interactions, transcriptional regulation, signaling, or metabolism may appear to have “unusual” properties. To quantify this, it is appropriate to randomize the network and test the hypothesis that the network is not statistically different from expected in a motivated ensemble. However, when dealing with metabolic networks, the randomization of the network using edge exchange generates fictitious reactions that are biochemically meaningless. Here we provide several natural ensembles of randomized metabolic networks. A first constraint is to use valid biochemical reactions. Further constraints correspond to imposing appropriate functional constraints. We explain how to perform these randomizations with the help of Markov Chain Monte Carlo (MCMC) and show that they allow one to approach the properties of biological metabolic networks. The implication of the present work is that the observed global structural properties of real metabolic networks are likely to be the consequence of simple biochemical and functional constraints. PMID:21779409

Network thermodynamic curation of human and yeast genome-scale metabolic models.

PubMed

Martínez, Verónica S; Quek, Lake-Ee; Nielsen, Lars K

2014-07-15

Genome-scale models are used for an ever-widening range of applications. Although there has been much focus on specifying the stoichiometric matrix, the predictive power of genome-scale models equally depends on reaction directions. Two-thirds of reactions in the two eukaryotic reconstructions Homo sapiens Recon 1 and Yeast 5 are specified as irreversible. However, these specifications are mainly based on biochemical textbooks or on their similarity to other organisms and are rarely underpinned by detailed thermodynamic analysis. In this study, a to our knowledge new workflow combining network-embedded thermodynamic and flux variability analysis was used to evaluate existing irreversibility constraints in Recon 1 and Yeast 5 and to identify new ones. A total of 27 and 16 new irreversible reactions were identified in Recon 1 and Yeast 5, respectively, whereas only four reactions were found with directions incorrectly specified against thermodynamics (three in Yeast 5 and one in Recon 1). The workflow further identified for both models several isolated internal loops that require further curation. The framework also highlighted the need for substrate channeling (in human) and ATP hydrolysis (in yeast) for the essential reaction catalyzed by phosphoribosylaminoimidazole carboxylase in purine metabolism. Finally, the framework highlighted differences in proline metabolism between yeast (cytosolic anabolism and mitochondrial catabolism) and humans (exclusively mitochondrial metabolism). We conclude that network-embedded thermodynamics facilitates the specification and validation of irreversibility constraints in compartmentalized metabolic models, at the same time providing further insight into network properties. Copyright © 2014 Biophysical Society. Published by Elsevier Inc. All rights reserved.

Genome-scale reconstruction of the metabolic network in Yersinia pestis CO92

NASA Astrophysics Data System (ADS)

Navid, Ali; Almaas, Eivind

2007-03-01

The gram-negative bacterium Yersinia pestis is the causative agent of bubonic plague. Using publicly available genomic, biochemical and physiological data, we have developed a constraint-based flux balance model of metabolism in the CO92 strain (biovar Orientalis) of this organism. The metabolic reactions were appropriately compartmentalized, and the model accounts for the exchange of metabolites, as well as the import of nutrients and export of waste products. We have characterized the metabolic capabilities and phenotypes of this organism, after comparing the model predictions with available experimental observations to evaluate accuracy and completeness. We have also begun preliminary studies into how cellular metabolism affects virulence.

Protein and metabolic engineering for the production of organic acids.

PubMed

Liu, Jingjing; Li, Jianghua; Shin, Hyun-Dong; Liu, Long; Du, Guocheng; Chen, Jian

2017-09-01

Organic acids are natural metabolites of living organisms. They have been widely applied in the food, pharmaceutical, and bio-based materials industries. In recent years, biotechnological routes to organic acids production from renewable raw materials have been regarded as very promising approaches. In this review, we provide an overview of current developments in the production of organic acids using protein and metabolic engineering strategies. The organic acids include propionic acid, pyruvate, itaconic acid, succinic acid, fumaric acid, malic acid and citric acid. We also expect that rapid developments in the fields of systems biology and synthetic biology will accelerate protein and metabolic engineering for microbial organic acid production in the future. Copyright © 2017. Published by Elsevier Ltd.

Metabolic engineering of Corynebacterium crenatium for enhancing production of higher alcohols

NASA Astrophysics Data System (ADS)

Su, Haifeng; Lin, Jiafu; Wang, Guangwei

2016-12-01

Biosynthesis approaches for the production of higher alcohols as a source of alternative fossil fuels have garnered increasing interest recently. However, there is little information available in the literature about using undirected whole-cell mutagenesis (UWCM) in vivo to improve higher alcohols production. In this study, for the first time, we approached this question from two aspects: first preferentially improving the capacity of expression host, and subsequently optimizing metabolic pathways using multiple genetic mutations to shift metabolic flux toward the biosynthetic pathway of target products to convert intermediate 2-keto acid compounds into diversified C4~C5 higher alcohols using UWCM in vivo, with the aim of improving the production. The results demonstrated the production of higher alcohols including isobutanol, 2-methyl-1-butanol, 3-methyl-1-butanol from glucose and duckweed under simultaneous saccharification and fermentation (SSF) scheme were higher based on the two aspects compared with only the use of wild-type stain as expression host. These findings showed that the improvement via UWCM in vivo in the two aspects for expression host and metabolic flux can facilitate the increase of higher alcohols production before using gene editing technology. Our work demonstrates that a multi-faceted approach for the engineering of novel synthetic pathways in microorganisms for improving biofuel production is feasible.

Systems metabolic engineering design: fatty acid production as an emerging case study.

PubMed

Tee, Ting Wei; Chowdhury, Anupam; Maranas, Costas D; Shanks, Jacqueline V

2014-05-01

Increasing demand for petroleum has stimulated industry to develop sustainable production of chemicals and biofuels using microbial cell factories. Fatty acids of chain lengths from C6 to C16 are propitious intermediates for the catalytic synthesis of industrial chemicals and diesel-like biofuels. The abundance of genetic information available for Escherichia coli and specifically, fatty acid metabolism in E. coli, supports this bacterium as a promising host for engineering a biocatalyst for the microbial production of fatty acids. Recent successes rooted in different features of systems metabolic engineering in the strain design of high-yielding medium chain fatty acid producing E. coli strains provide an emerging case study of design methods for effective strain design. Classical metabolic engineering and synthetic biology approaches enabled different and distinct design paths towards a high-yielding strain. Here we highlight a rational strain design process in systems biology, an integrated computational and experimental approach for carboxylic acid production, as an alternative method. Additional challenges inherent in achieving an optimal strain for commercialization of medium chain-length fatty acids will likely require a collection of strategies from systems metabolic engineering. Not only will the continued advancement in systems metabolic engineering result in these highly productive strains more quickly, this knowledge will extend more rapidly the carboxylic acid platform to the microbial production of carboxylic acids with alternate chain-lengths and functionalities. © 2014 Wiley Periodicals, Inc.

Urban Economies Resource Productivity and Decoupling: Metabolism Trends of 1996-2011 in Sweden, Stockholm, and Gothenburg.

PubMed

Kalmykova, Yuliya; Rosado, Leonardo; Patrício, João

2015-07-21

Resource productivity and evidence of economic decoupling were investigated on the basis of the time series in 1996-2011 of material flow analysis for Sweden, Stockholm, and Gothenburg. In the three cases, absolute reductions in CO2 emissions by about 20% were observed, energy consumption per capita decreased, while gross domestic product (GDP) per capita grew. The energy consumption of the residential and public sectors decreased drastically, while the transport energy consumption is still growing steadily. Decoupling of the economy as a whole (i.e., including materials) is not yet happening at any scale. The domestic material consumption (DMC) continues to increase, in parallel with the GDP. The rate of increase for DMC is slower than that for GDP in both Stockholm and Sweden as a whole (i.e., relative decoupling). The metabolism of the cities does not replicate the national metabolism, and the two cities each have their own distinct metabolism profiles. As a consequence, policy implications for each of the case studies were suggested. In general, because of the necessarily different roles of the two cities in the national economy, generic resource productivity benchmarks, such as CO2 per capita, should be avoided in favor of sectorial benchmarks, such as industry, transport, or residential CO2 per capita. In addition, the share of the city impacts caused by the provision of a service for the rest of the country, such as a port, could be allocated to the national economy.

A refined genome-scale reconstruction of Chlamydomonas metabolism provides a platform for systems-level analyses.

PubMed

Imam, Saheed; Schäuble, Sascha; Valenzuela, Jacob; López García de Lomana, Adrián; Carter, Warren; Price, Nathan D; Baliga, Nitin S

2015-12-01

Microalgae have reemerged as organisms of prime biotechnological interest due to their ability to synthesize a suite of valuable chemicals. To harness the capabilities of these organisms, we need a comprehensive systems-level understanding of their metabolism, which can be fundamentally achieved through large-scale mechanistic models of metabolism. In this study, we present a revised and significantly improved genome-scale metabolic model for the widely-studied microalga, Chlamydomonas reinhardtii. The model, iCre1355, represents a major advance over previous models, both in content and predictive power. iCre1355 encompasses a broad range of metabolic functions encoded across the nuclear, chloroplast and mitochondrial genomes accounting for 1355 genes (1460 transcripts), 2394 and 1133 metabolites. We found improved performance over the previous metabolic model based on comparisons of predictive accuracy across 306 phenotypes (from 81 mutants), lipid yield analysis and growth rates derived from chemostat-grown cells (under three conditions). Measurement of macronutrient uptake revealed carbon and phosphate to be good predictors of growth rate, while nitrogen consumption appeared to be in excess. We analyzed high-resolution time series transcriptomics data using iCre1355 to uncover dynamic pathway-level changes that occur in response to nitrogen starvation and changes in light intensity. This approach enabled accurate prediction of growth rates, the cessation of growth and accumulation of triacylglycerols during nitrogen starvation, and the temporal response of different growth-associated pathways to increased light intensity. Thus, iCre1355 represents an experimentally validated genome-scale reconstruction of C. reinhardtii metabolism that should serve as a useful resource for studying the metabolic processes of this and related microalgae. © 2015 The Authors The Plant Journal © 2015 John Wiley & Sons Ltd.

Genome-scale model-driven strain design for dicarboxylic acid production in Yarrowia lipolytica.

PubMed

Mishra, Pranjul; Lee, Na-Rae; Lakshmanan, Meiyappan; Kim, Minsuk; Kim, Byung-Gee; Lee, Dong-Yup

2018-03-19

Recently, there have been several attempts to produce long-chain dicarboxylic acids (DCAs) in various microbial hosts. Of these, Yarrowia lipolytica has great potential due to its oleaginous characteristics and unique ability to utilize hydrophobic substrates. However, Y. lipolytica should be further engineered to make it more competitive: the current approaches are mostly intuitive and cumbersome, thus limiting its industrial application. In this study, we proposed model-guided metabolic engineering strategies for enhanced production of DCAs in Y. lipolytica. At the outset, we reconstructed genome-scale metabolic model (GSMM) of Y. lipolytica (iYLI647) by substantially expanding the previous models. Subsequently, the model was validated using three sets of published culture experiment data. It was finally exploited to identify genetic engineering targets for overexpression, knockout, and cofactor modification by applying several in silico strain design methods, which potentially give rise to high yield production of the industrially relevant long-chain DCAs, e.g., dodecanedioic acid (DDDA). The resultant targets include (1) malate dehydrogenase and malic enzyme genes and (2) glutamate dehydrogenase gene, in silico overexpression of which generated additional NADPH required for fatty acid synthesis, leading to the increased DDDA fluxes by 48% and 22% higher, respectively, compared to wild-type. We further investigated the effect of supplying branched-chain amino acids on the acetyl-CoA turn-over rate which is key metabolite for fatty acid synthesis, suggesting their significance for production of DDDA in Y. lipolytica. In silico model-based strain design strategies allowed us to identify several metabolic engineering targets for overproducing DCAs in lipid accumulating yeast, Y. lipolytica. Thus, the current study can provide a methodological framework that is applicable to other oleaginous yeasts for value-added biochemical production.

Microbial isoprenoid production: an example of green chemistry through metabolic engineering.

PubMed

Maury, Jérôme; Asadollahi, Mohammad A; Møller, Kasper; Clark, Anthony; Nielsen, Jens

2005-01-01

Saving energy, cost efficiency, producing less waste, improving the biodegradability of products, potential for producing novel and complex molecules with improved properties, and reducing the dependency on fossil fuels as raw materials are the main advantages of using biotechnological processes to produce chemicals. Such processes are often referred to as green chemistry or white biotechnology. Metabolic engineering, which permits the rational design of cell factories using directed genetic modifications, is an indispensable strategy for expanding green chemistry. In this chapter, the benefits of using metabolic engineering approaches for the development of green chemistry are illustrated by the recent advances in microbial production of isoprenoids, a diverse and important group of natural compounds with numerous existing and potential commercial applications. Accumulated knowledge on the metabolic pathways leading to the synthesis of the principal precursors of isoprenoids is reviewed, and recent investigations into isoprenoid production using engineered cell factories are described.

Advances in the integration of transcriptional regulatory information into genome-scale metabolic models.

PubMed

Vivek-Ananth, R P; Samal, Areejit

2016-09-01

A major goal of systems biology is to build predictive computational models of cellular metabolism. Availability of complete genome sequences and wealth of legacy biochemical information has led to the reconstruction of genome-scale metabolic networks in the last 15 years for several organisms across the three domains of life. Due to paucity of information on kinetic parameters associated with metabolic reactions, the constraint-based modelling approach, flux balance analysis (FBA), has proved to be a vital alternative to investigate the capabilities of reconstructed metabolic networks. In parallel, advent of high-throughput technologies has led to the generation of massive amounts of omics data on transcriptional regulation comprising mRNA transcript levels and genome-wide binding profile of transcriptional regulators. A frontier area in metabolic systems biology has been the development of methods to integrate the available transcriptional regulatory information into constraint-based models of reconstructed metabolic networks in order to increase the predictive capabilities of computational models and understand the regulation of cellular metabolism. Here, we review the existing methods to integrate transcriptional regulatory information into constraint-based models of metabolic networks. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Tailoring tobacco hairy root metabolism for the production of stilbenes.

PubMed

Hidalgo, Diego; Georgiev, Milen; Marchev, Andrey; Bru-Martínez, Roque; Cusido, Rosa M; Corchete, Purificación; Palazon, Javier

2017-12-21

Tobacco hairy root (HR) cultures, which have been widely used for the heterologous production of target compounds, have an innate capacity to bioconvert exogenous t-resveratrol (t-R) into t-piceatannol (t-Pn) and t-pterostilbene (t-Pt). We established genetically engineered HR carrying the gene encoding stilbene synthase (STS) from Vitis vinifera and/or the transcription factor (TF) AtMYB12 from Arabidopsis thaliana, in order to generate a holistic response in the phenylpropanoid pathway and coordinate the up-regulation of multiple metabolic steps. Additionally, an artificial microRNA for chalcone synthase (amiRNA CHS) was utilized to arrest the normal flux through the endogenous chalcone synthase (CHS) enzyme, which would otherwise compete for precursors with the STS enzyme imported for the flux deviation. The transgenic HR were able to biosynthesize the target stilbenes, achieving a production of 40 μg L -1 of t-R, which was partially metabolized into t-Pn and t-Pt (up to 2.2 μg L -1 and 86.4 μg L -1 , respectively), as well as its glucoside piceid (up to 339.7 μg L -1 ). Major metabolic perturbations were caused by the TF AtMYB12, affecting both primary and secondary metabolism, which confirms the complexity of biotechnological systems based on seed plant in vitro cultures for the heterologous production of high-value molecules.

Programming Saposin-Mediated Compensatory Metabolic Sinks for Enhanced Ubiquinone Production.

PubMed

Xu, Wen; Yuan, Jifeng; Yang, Shuiyun; Ching, Chi-Bun; Liu, Jiankang

2016-12-16

Microbial synthesis of ubiquinone by fermentation processes has been emerging in recent years. However, as ubiquinone is a primary metabolite that is tightly regulated by the host central metabolism, tweaking the individual pathway components could only result in a marginal improvement on the ubiquinone production. Given that ubiquinone is stored in the lipid bilayer, we hypothesized that introducing additional metabolic sink for storing ubiquinone might improve the CoQ 10 production. As human lipid binding/transfer protein saposin B (hSapB) was reported to extract ubiquinone from the lipid bilayer and form the water-soluble complex, hSapB was chosen to build a compensatory metabolic sink for the ubiquinone storage. As a proof-of-concept, hSapB-mediated metabolic sink systems were devised and systematically investigated in the model organism of Escherichia coli. The hSapB-mediated periplasmic sink resulted in more than 200% improvement of CoQ 8 over the wild type strain. Further investigation revealed that hSapB-mediated sink systems could also improve the CoQ 10 production in a CoQ 10 -hyperproducing E. coli strain obtained by a modular pathway rewiring approach. As the design principles and the engineering strategies reported here are generalizable to other microbes, compensatory sink systems will be a method of significant interest to the synthetic biology community.

Diatom cultivation and biotechnologically relevant products. Part I: cultivation at various scales.

PubMed

Lebeau, Thierry; Robert, Jean-Michel

2003-02-01

Biotechnological applications of diatoms are still in development. Further development at the industrial scale will depend on optimisation of the culture process with the aim of reducing costs. Because of the photoautotrophic status of the majority of diatoms, microalgal cultures suffer from the limitation of light diffusion, which requires the development of suitable photobioreactors. Thus, genetically engineered microalgae that may be cultivated in heterotrophic conditions present a new opportunity. Other limiting factors, such as nutrients (phosphate, nitrogen, silicon), pH, temperature, bioturbation and many more must be taken into account. Most of the time, metabolic stress conditions lead to an overproduction of the products of interest, with a decrease in biomass production as a consequence. Outdoor cultures in open ponds are usually devoted to aquaculture for the feeding of shrimps and bivalve molluscs (commercial production), while closed axenic indoor/outdoor photobioreactors are used for biotechnological compounds of homogeneous composition (still at the laboratory scale). In addition to the optimum culture conditions that have to be taken into account for photobioreactor design, the localisation of produced metabolites (intra- or extracellular) may also be taken into account when choosing the design. Microalgal cell immobilisation may be a suitable technique for application to benthic diatoms, which are usually sensitive to bioturbation and/or metabolites which may be overexpressed.

Genome-scale model reveals metabolic basis of biomass partitioning in a model diatom

DOE PAGES

Levering, Jennifer; Broddrick, Jared; Dupont, Christopher L.; ...

2016-05-06

Diatoms are eukaryotic microalgae that contain genes from various sources, including bacteria and the secondary endosymbiotic host. Due to this unique combination of genes, diatoms are taxonomically and functionally distinct from other algae and vascular plants and confer novel metabolic capabilities. Based on the genome annotation, we performed a genome-scale metabolic network reconstruction for the marine diatom Phaeodactylum tricornutum. Due to their endosymbiotic origin, diatoms possess a complex chloroplast structure which complicates the prediction of subcellular protein localization. Based on previous work we implemented a pipeline that exploits a series of bioinformatics tools to predict protein localization. The manually curatedmore » reconstructed metabolic network iLB1027_lipid accounts for 1,027 genes associated with 4,456 reactions and 2,172 metabolites distributed across six compartments. To constrain the genome-scale model, we determined the organism specific biomass composition in terms of lipids, carbohydrates, and proteins using Fourier transform infrared spectrometry. Our simulations indicate the presence of a yet unknown glutamine-ornithine shunt that could be used to transfer reducing equivalents generated by photosynthesis to the mitochondria. Furthermore, the model reflects the known biochemical composition of P. tricornutum in defined culture conditions and enables metabolic engineering strategies to improve the use of P. tricornutum for biotechnological applications.« less

Metabolic engineering of Corynebacterium crenatium for enhancing production of higher alcohols

PubMed Central

Su, Haifeng; Lin, Jiafu; Wang, GuangWei

2016-01-01

Biosynthesis approaches for the production of higher alcohols as a source of alternative fossil fuels have garnered increasing interest recently. However, there is little information available in the literature about using undirected whole-cell mutagenesis (UWCM) in vivo to improve higher alcohols production. In this study, for the first time, we approached this question from two aspects: first preferentially improving the capacity of expression host, and subsequently optimizing metabolic pathways using multiple genetic mutations to shift metabolic flux toward the biosynthetic pathway of target products to convert intermediate 2-keto acid compounds into diversified C4~C5 higher alcohols using UWCM in vivo, with the aim of improving the production. The results demonstrated the production of higher alcohols including isobutanol, 2-methyl-1-butanol, 3-methyl-1-butanol from glucose and duckweed under simultaneous saccharification and fermentation (SSF) scheme were higher based on the two aspects compared with only the use of wild-type stain as expression host. These findings showed that the improvement via UWCM in vivo in the two aspects for expression host and metabolic flux can facilitate the increase of higher alcohols production before using gene editing technology. Our work demonstrates that a multi-faceted approach for the engineering of novel synthetic pathways in microorganisms for improving biofuel production is feasible. PMID:27996038

Engineering microorganisms to increase ethanol production by metabolic redirection

DOEpatents

Deng, Yu; Olson, Daniel G.; van Dijken, Johannes Pieter; Shaw, IV, Arthur J.; Argyros, Aaron; Barrett, Trisha; Caiazza, Nicky; Herring, Christopher D.; Rogers, Stephen R.; Agbogbo, Frank

2017-10-31

The present invention provides for the manipulation of carbon flux in a recombinant host cell to increase the formation of desirable products. The invention relates to cellulose-digesting organisms that have been genetically modified to allow the production of ethanol at a high yield by redirecting carbon flux at key steps of central metabolism.

Mass-Specific Metabolic Rate Influences Sperm Performance through Energy Production in Mammals

PubMed Central

Tourmente, Maximiliano; Roldan, Eduardo R. S.

2015-01-01

Mass-specific metabolic rate, the rate at which organisms consume energy per gram of body weight, is negatively associated with body size in metazoans. As a consequence, small species have higher cellular metabolic rates and are able to process resources at a faster rate than large species. Since mass-specific metabolic rate has been shown to constrain evolution of sperm traits, and most of the metabolic activity of sperm cells relates to ATP production for sperm motility, we hypothesized that mass-specific metabolic rate could influence sperm energetic metabolism at the cellular level if sperm cells maintain the metabolic rate of organisms that generate them. We compared data on sperm straight-line velocity, mass-specific metabolic rate, and sperm ATP content from 40 mammalian species and found that the mass-specific metabolic rate positively influences sperm swimming velocity by (a) an indirect effect of sperm as the result of an increased sperm length, and (b) a direct effect independent of sperm length. In addition, our analyses show that species with higher mass-specific metabolic rate have higher ATP content per sperm and higher concentration of ATP per μm of sperm length, which are positively associated with sperm velocity. In conclusion, our results suggest that species with high mass-specific metabolic rate have been able to evolve both long and fast sperm. Moreover, independently of its effect on the production of larger sperm, the mass-specific metabolic rate is able to influence sperm velocity by increasing sperm ATP content in mammals. PMID:26371474

Metabolic Plasticity and Inter-Compartmental Interactions in Rice Metabolism: An Analysis from Reaction Deletion Study.

PubMed

Shaw, Rahul; Kundu, Sudip

2015-01-01

More than 20% of the total caloric intake of human population comes from rice. The expression of rice genes and hence, the concentration of enzymatic proteins might vary due to several biotic and abiotic stresses. It in turn, can influence the overall metabolism and survivability of rice plant. Thus, understanding the rice cellular metabolism, its plasticity and potential readjustments under different perturbations can help rice biotechnologists to design efficient rice cultivars. Here, using the flux balance analysis (FBA) method, with the help of in-silico reaction deletion strategy, we study the metabolic plasticity of genome-scale metabolic model of rice leaf. A set of 131 reactions, essential for the production of primary biomass precursors is identified; deletion of any of them can inhibit the overall biomass production. Usability Index (IU) for the rest of the reactions are estimated and based on this parameter, they are classified into three categories-maximally-favourable, quasi-favourable and unfavourable for the primary biomass production. The lower value of 1 - IU of a reaction suggests that the cell cannot easily bypass it for biomass production. While some of the alternative paths are energetically equally efficient, others demand for higher photon. The variations in (i) ATP/NADPH ratio, (ii) exchange of metabolites through chloroplastic transporters and (iii) total biomass production are also presented here. Mutual metabolic dependencies of different cellular compartments are also demonstrated.

Perturbation Experiments: Approaches for Metabolic Pathway Analysis in Bioreactors.

PubMed

Weiner, Michael; Tröndle, Julia; Albermann, Christoph; Sprenger, Georg A; Weuster-Botz, Dirk

2016-01-01

In the last decades, targeted metabolic engineering of microbial cells has become one of the major tools in bioprocess design and optimization. For successful application, a detailed knowledge is necessary about the relevant metabolic pathways and their regulation inside the cells. Since in vitro experiments cannot display process conditions and behavior properly, process data about the cells' metabolic state have to be collected in vivo. For this purpose, special techniques and methods are necessary. Therefore, most techniques enabling in vivo characterization of metabolic pathways rely on perturbation experiments, which can be divided into dynamic and steady-state approaches. To avoid any process disturbance, approaches which enable perturbation of cell metabolism in parallel to the continuing production process are reasonable. Furthermore, the fast dynamics of microbial production processes amplifies the need of parallelized data generation. These points motivate the development of a parallelized approach for multiple metabolic perturbation experiments outside the operating production reactor. An appropriate approach for in vivo characterization of metabolic pathways is presented and applied exemplarily to a microbial L-phenylalanine production process on a 15 L-scale.

Metabolic evolution of Corynebacterium glutamicum for increased production of L-ornithine

PubMed Central

2013-01-01

Background L-ornithine is effective in the treatment of liver diseases and helps strengthen the heart. The commercial applications mean that efficient biotechnological production of L-ornithine has become increasingly necessary. Adaptive evolution strategies have been proven a feasible and efficient technique to achieve improved cellular properties without requiring metabolic or regulatory details of the strain. The evolved strains can be further optimised by metabolic engineering. Thus, metabolic evolution strategy was used for engineering Corynebacterium glutamicum to enhance L-ornithine production. Results A C. glutamicum strain was engineered by using a combination of gene deletions and adaptive evolution with 70 passages of growth-based selection. The metabolically evolved C. glutamicum strain, named ΔAPE6937R42, produced 24.1 g/L of L-ornithine in a 5-L bioreactor. The mechanism used by C. glutamicum ΔAPE6937R42 to produce L-ornithine was investigated by analysing transcriptional levels of select genes and NADPH contents. The upregulation of the transcription levels of genes involved in the upstream pathway of glutamate biosynthesis and the elevated NADPH concentration caused by the upregulation of the transcriptional level of the ppnK gene promoted L-ornithine production in C. glutamicum ΔAPE6937R42. Conclusions The availability of NADPH plays an important role in L-ornithine production in C. glutamicum. Our results demonstrated that the combination of growth-coupled evolution with analysis of transcript abundances provides a strategy to engineer microbial strains for improving production of target compounds. PMID:23725060

Flux analysis and metabolomics for systematic metabolic engineering of microorganisms.

PubMed

Toya, Yoshihiro; Shimizu, Hiroshi

2013-11-01

Rational engineering of metabolism is important for bio-production using microorganisms. Metabolic design based on in silico simulations and experimental validation of the metabolic state in the engineered strain helps in accomplishing systematic metabolic engineering. Flux balance analysis (FBA) is a method for the prediction of metabolic phenotype, and many applications have been developed using FBA to design metabolic networks. Elementary mode analysis (EMA) and ensemble modeling techniques are also useful tools for in silico strain design. The metabolome and flux distribution of the metabolic pathways enable us to evaluate the metabolic state and provide useful clues to improve target productivity. Here, we reviewed several computational applications for metabolic engineering by using genome-scale metabolic models of microorganisms. We also discussed the recent progress made in the field of metabolomics and (13)C-metabolic flux analysis techniques, and reviewed these applications pertaining to bio-production development. Because these in silico or experimental approaches have their respective advantages and disadvantages, the combined usage of these methods is complementary and effective for metabolic engineering. Copyright © 2013 Elsevier Inc. All rights reserved.

Integration and Validation of the Genome-Scale Metabolic Models of Pichia pastoris: A Comprehensive Update of Protein Glycosylation Pathways, Lipid and Energy Metabolism

PubMed Central

Tomàs-Gamisans, Màrius; Ferrer, Pau; Albiol, Joan

2016-01-01

Motivation Genome-scale metabolic models (GEMs) are tools that allow predicting a phenotype from a genotype under certain environmental conditions. GEMs have been developed in the last ten years for a broad range of organisms, and are used for multiple purposes such as discovering new properties of metabolic networks, predicting new targets for metabolic engineering, as well as optimizing the cultivation conditions for biochemicals or recombinant protein production. Pichia pastoris is one of the most widely used organisms for heterologous protein expression. There are different GEMs for this methylotrophic yeast of which the most relevant and complete in the published literature are iPP668, PpaMBEL1254 and iLC915. However, these three models differ regarding certain pathways, terminology for metabolites and reactions and annotations. Moreover, GEMs for some species are typically built based on the reconstructed models of related model organisms. In these cases, some organism-specific pathways could be missing or misrepresented. Results In order to provide an updated and more comprehensive GEM for P. pastoris, we have reconstructed and validated a consensus model integrating and merging all three existing models. In this step a comprehensive review and integration of the metabolic pathways included in each one of these three versions was performed. In addition, the resulting iMT1026 model includes a new description of some metabolic processes. Particularly new information described in recently published literature is included, mainly related to fatty acid and sphingolipid metabolism, glycosylation and cell energetics. Finally the reconstructed model was tested and validated, by comparing the results of the simulations with available empirical physiological datasets results obtained from a wide range of experimental conditions, such as different carbon sources, distinct oxygen availability conditions, as well as producing of two different recombinant proteins. In

Analysis of Aspergillus nidulans metabolism at the genome-scale

PubMed Central

David, Helga; Özçelik, İlknur Ş; Hofmann, Gerald; Nielsen, Jens

2008-01-01

Background Aspergillus nidulans is a member of a diverse group of filamentous fungi, sharing many of the properties of its close relatives with significance in the fields of medicine, agriculture and industry. Furthermore, A. nidulans has been a classical model organism for studies of development biology and gene regulation, and thus it has become one of the best-characterized filamentous fungi. It was the first Aspergillus species to have its genome sequenced, and automated gene prediction tools predicted 9,451 open reading frames (ORFs) in the genome, of which less than 10% were assigned a function. Results In this work, we have manually assigned functions to 472 orphan genes in the metabolism of A. nidulans, by using a pathway-driven approach and by employing comparative genomics tools based on sequence similarity. The central metabolism of A. nidulans, as well as biosynthetic pathways of relevant secondary metabolites, was reconstructed based on detailed metabolic reconstructions available for A. niger and Saccharomyces cerevisiae, and information on the genetics, biochemistry and physiology of A. nidulans. Thereby, it was possible to identify metabolic functions without a gene associated, and to look for candidate ORFs in the genome of A. nidulans by comparing its sequence to sequences of well-characterized genes in other species encoding the function of interest. A classification system, based on defined criteria, was developed for evaluating and selecting the ORFs among the candidates, in an objective and systematic manner. The functional assignments served as a basis to develop a mathematical model, linking 666 genes (both previously and newly annotated) to metabolic roles. The model was used to simulate metabolic behavior and additionally to integrate, analyze and interpret large-scale gene expression data concerning a study on glucose repression, thereby providing a means of upgrading the information content of experimental data and getting further

In silico metabolic engineering of Clostridium ljungdahlii for synthesis gas fermentation.

PubMed

Chen, Jin; Henson, Michael A

2016-11-01

Synthesis gas fermentation is one of the most promising routes to convert synthesis gas (syngas; mainly comprised of H 2 and CO) to renewable liquid fuels and chemicals by specialized bacteria. The most commonly studied syngas fermenting bacterium is Clostridium ljungdahlii, which produces acetate and ethanol as its primary metabolic byproducts. Engineering of C. ljungdahlii metabolism to overproduce ethanol, enhance the synthesize of the native byproducts lactate and 2,3-butanediol, and introduce the synthesis of non-native products such as butanol and butyrate has substantial commercial value. We performed in silico metabolic engineering studies using a genome-scale reconstruction of C. ljungdahlii metabolism and the OptKnock computational framework to identify gene knockouts that were predicted to enhance the synthesis of these native products and non-native products, introduced through insertion of the necessary heterologous pathways. The OptKnock derived strategies were often difficult to assess because increase product synthesis was invariably accompanied by decreased growth. Therefore, the OptKnock strategies were further evaluated using a spatiotemporal metabolic model of a syngas bubble column reactor, a popular technology for large-scale gas fermentation. Unlike flux balance analysis, the bubble column model accounted for the complex tradeoffs between increased product synthesis and reduced growth rates of engineered mutants within the spatially varying column environment. The two-stage methodology for deriving and evaluating metabolic engineering strategies was shown to yield new C. ljungdahlii gene targets that offer the potential for increased product synthesis under realistic syngas fermentation conditions. Copyright © 2016 International Metabolic Engineering Society. Published by Elsevier Inc. All rights reserved.

A link between hepatic glucose production and peripheral energy metabolism via hepatokines

PubMed Central

Abdul-Wahed, Aya; Gautier-Stein, Amandine; Casteras, Sylvie; Soty, Maud; Roussel, Damien; Romestaing, Caroline; Guillou, Hervé; Tourette, Jean-André; Pleche, Nicolas; Zitoun, Carine; Gri, Blandine; Sardella, Anne; Rajas, Fabienne; Mithieux, Gilles

2014-01-01

Type 2 diabetes is characterized by a deterioration of glucose tolerance, which associates insulin resistance of glucose uptake by peripheral tissues and increased endogenous glucose production. Here we report that the specific suppression of hepatic glucose production positively modulates whole-body glucose and energy metabolism. We used mice deficient in liver glucose-6 phosphatase that is mandatory for endogenous glucose production. When they were fed a high fat/high sucrose diet, they resisted the development of diabetes and obesity due to the activation of peripheral glucose metabolism and thermogenesis. This was linked to the secretion of hepatic hormones like fibroblast growth factor 21 and angiopoietin-like factor 6. Interestingly, the deletion of hepatic glucose-6 phosphatase in previously obese and insulin-resistant mice resulted in the rapid restoration of glucose and body weight controls. Therefore, hepatic glucose production is an essential lever for the control of whole-body energy metabolism during the development of obesity and diabetes. PMID:25061558

A link between hepatic glucose production and peripheral energy metabolism via hepatokines.

PubMed

Abdul-Wahed, Aya; Gautier-Stein, Amandine; Casteras, Sylvie; Soty, Maud; Roussel, Damien; Romestaing, Caroline; Guillou, Hervé; Tourette, Jean-André; Pleche, Nicolas; Zitoun, Carine; Gri, Blandine; Sardella, Anne; Rajas, Fabienne; Mithieux, Gilles

2014-08-01

Type 2 diabetes is characterized by a deterioration of glucose tolerance, which associates insulin resistance of glucose uptake by peripheral tissues and increased endogenous glucose production. Here we report that the specific suppression of hepatic glucose production positively modulates whole-body glucose and energy metabolism. We used mice deficient in liver glucose-6 phosphatase that is mandatory for endogenous glucose production. When they were fed a high fat/high sucrose diet, they resisted the development of diabetes and obesity due to the activation of peripheral glucose metabolism and thermogenesis. This was linked to the secretion of hepatic hormones like fibroblast growth factor 21 and angiopoietin-like factor 6. Interestingly, the deletion of hepatic glucose-6 phosphatase in previously obese and insulin-resistant mice resulted in the rapid restoration of glucose and body weight controls. Therefore, hepatic glucose production is an essential lever for the control of whole-body energy metabolism during the development of obesity and diabetes.

Sustainable source of omega-3 eicosapentaenoic acid from metabolically engineered Yarrowia lipolytica: from fundamental research to commercial production.

PubMed

Xie, Dongming; Jackson, Ethel N; Zhu, Quinn

2015-02-01

The omega-3 fatty acids, cis-5, 8, 11, 14, and 17-eicosapentaenoic acid (C20:5; EPA) and cis-4, 7, 10, 13, 16, and 19-docosahexaenoic acid (C22:6; DHA), have wide-ranging benefits in improving heart health, immune function, mental health, and infant cognitive development. Currently, the major source for EPA and DHA is from fish oil, and a minor source of DHA is from microalgae. With the increased demand for EPA and DHA, DuPont has developed a clean and sustainable source of the omega-3 fatty acid EPA through fermentation using metabolically engineered strains of Yarrowia lipolytica. In this mini-review, we will focus on DuPont's technology for EPA production. Specifically, EPA biosynthetic and supporting pathways have been introduced into the oleaginous yeast to synthesize and accumulate EPA under fermentation conditions. This Yarrowia platform can also produce tailored omega-3 (EPA, DHA) and/or omega-6 (ARA, GLA) fatty acid mixtures in the cellular lipid profiles. Fundamental research such as metabolic engineering for strain construction, high-throughput screening for strain selection, fermentation process development, and process scale-up were all needed to achieve the high levels of EPA titer, rate, and yield required for commercial application. Here, we summarize how we have combined the fundamental bioscience and the industrial engineering skills to achieve large-scale production of Yarrowia biomass containing high amounts of EPA, which led to two commercial products, New Harvest™ EPA oil and Verlasso® salmon.

Comprehensive reconstruction and in silico analysis of Aspergillus niger genome-scale metabolic network model that accounts for 1210 ORFs.

PubMed

Lu, Hongzhong; Cao, Weiqiang; Ouyang, Liming; Xia, Jianye; Huang, Mingzhi; Chu, Ju; Zhuang, Yingping; Zhang, Siliang; Noorman, Henk

2017-03-01

Aspergillus niger is one of the most important cell factories for industrial enzymes and organic acids production. A comprehensive genome-scale metabolic network model (GSMM) with high quality is crucial for efficient strain improvement and process optimization. The lack of accurate reaction equations and gene-protein-reaction associations (GPRs) in the current best model of A. niger named GSMM iMA871, however, limits its application scope. To overcome these limitations, we updated the A. niger GSMM by combining the latest genome annotation and literature mining technology. Compared with iMA871, the number of reactions in iHL1210 was increased from 1,380 to 1,764, and the number of unique ORFs from 871 to 1,210. With the aid of our transcriptomics analysis, the existence of 63% ORFs and 68% reactions in iHL1210 can be verified when glucose was used as the only carbon source. Physiological data from chemostat cultivations, 13 C-labeled and molecular experiments from the published literature were further used to check the performance of iHL1210. The average correlation coefficients between the predicted fluxes and estimated fluxes from 13 C-labeling data were sufficiently high (above 0.89) and the prediction of cell growth on most of the reported carbon and nitrogen sources was consistent. Using the updated genome-scale model, we evaluated gene essentiality on synthetic and yeast extract medium, as well as the effects of NADPH supply on glucoamylase production in A. niger. In summary, the new A. niger GSMM iHL1210 contains significant improvements with respect to the metabolic coverage and prediction performance, which paves the way for systematic metabolic engineering of A. niger. Biotechnol. Bioeng. 2017;114: 685-695. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

Multi-scale modeling of Arabidopsis thaliana response to different CO2 conditions: From gene expression to metabolic flux.

PubMed

Liu, Lin; Shen, Fangzhou; Xin, Changpeng; Wang, Zhuo

2016-01-01

Multi-scale investigation from gene transcript level to metabolic activity is important to uncover plant response to environment perturbation. Here we integrated a genome-scale constraint-based metabolic model with transcriptome data to explore Arabidopsis thaliana response to both elevated and low CO2 conditions. The four condition-specific models from low to high CO2 concentrations show differences in active reaction sets, enriched pathways for increased/decreased fluxes, and putative post-transcriptional regulation, which indicates that condition-specific models are necessary to reflect physiological metabolic states. The simulated CO2 fixation flux at different CO2 concentrations is consistent with the measured Assimilation-CO2intercellular curve. Interestingly, we found that reactions in primary metabolism are affected most significantly by CO2 perturbation, whereas secondary metabolic reactions are not influenced a lot. The changes predicted in key pathways are consistent with existing knowledge. Another interesting point is that Arabidopsis is required to make stronger adjustment on metabolism to adapt to the more severe low CO2 stress than elevated CO2 . The challenges of identifying post-transcriptional regulation could also be addressed by the integrative model. In conclusion, this innovative application of multi-scale modeling in plants demonstrates potential to uncover the mechanisms of metabolic response to different conditions. © 2015 Institute of Botany, Chinese Academy of Sciences.

C4GEM, a Genome-Scale Metabolic Model to Study C4 Plant Metabolism1[W][OA

PubMed Central

de Oliveira Dal’Molin, Cristiana Gomes; Quek, Lake-Ee; Palfreyman, Robin William; Brumbley, Stevens Michael; Nielsen, Lars Keld

2010-01-01

Leaves of C4 grasses (such as maize [Zea mays], sugarcane [Saccharum officinarum], and sorghum [Sorghum bicolor]) form a classical Kranz leaf anatomy. Unlike C3 plants, where photosynthetic CO2 fixation proceeds in the mesophyll (M), the fixation process in C4 plants is distributed between two cell types, the M cell and the bundle sheath (BS) cell. Here, we develop a C4 genome-scale model (C4GEM) for the investigation of flux distribution in M and BS cells during C4 photosynthesis. C4GEM, to our knowledge, is the first large-scale metabolic model that encapsulates metabolic interactions between two different cell types. C4GEM is based on the Arabidopsis (Arabidopsis thaliana) model (AraGEM) but has been extended by adding reactions and transporters responsible to represent three different C4 subtypes (NADP-ME [for malic enzyme], NAD-ME, and phosphoenolpyruvate carboxykinase). C4GEM has been validated for its ability to synthesize 47 biomass components and consists of 1,588 unique reactions, 1,755 metabolites, 83 interorganelle transporters, and 29 external transporters (including transport through plasmodesmata). Reactions in the common C4 model have been associated with well-annotated C4 species (NADP-ME subtypes): 3,557 genes in sorghum, 11,623 genes in maize, and 3,881 genes in sugarcane. The number of essential reactions not assigned to genes is 131, 135, and 156 in sorghum, maize, and sugarcane, respectively. Flux balance analysis was used to assess the metabolic activity in M and BS cells during C4 photosynthesis. Our simulations were consistent with chloroplast proteomic studies, and C4GEM predicted the classical C4 photosynthesis pathway and its major effect in organelle function in M and BS. The model also highlights differences in metabolic activities around photosystem I and photosystem II for three different C4 subtypes. Effects of CO2 leakage were also explored. C4GEM is a viable framework for in silico analysis of cell cooperation between M and BS

Metabolic Engineering of Oleaginous Yeasts for Production of Fuels and Chemicals

PubMed Central

Shi, Shuobo; Zhao, Huimin

2017-01-01

Oleaginous yeasts have been increasingly explored for production of chemicals and fuels via metabolic engineering. Particularly, there is a growing interest in using oleaginous yeasts for the synthesis of lipid-related products due to their high lipogenesis capability, robustness, and ability to utilize a variety of substrates. Most of the metabolic engineering studies in oleaginous yeasts focused on Yarrowia that already has plenty of genetic engineering tools. However, recent advances in systems biology and synthetic biology have provided new strategies and tools to engineer those oleaginous yeasts that have naturally high lipid accumulation but lack genetic tools, such as Rhodosporidium, Trichosporon, and Lipomyces. This review highlights recent accomplishments in metabolic engineering of oleaginous yeasts and recent advances in the development of genetic engineering tools in oleaginous yeasts within the last 3 years. PMID:29167664

Metabolic Engineering of Oleaginous Yeasts for Production of Fuels and Chemicals.

PubMed

Shi, Shuobo; Zhao, Huimin

2017-01-01

Oleaginous yeasts have been increasingly explored for production of chemicals and fuels via metabolic engineering. Particularly, there is a growing interest in using oleaginous yeasts for the synthesis of lipid-related products due to their high lipogenesis capability, robustness, and ability to utilize a variety of substrates. Most of the metabolic engineering studies in oleaginous yeasts focused on Yarrowia that already has plenty of genetic engineering tools. However, recent advances in systems biology and synthetic biology have provided new strategies and tools to engineer those oleaginous yeasts that have naturally high lipid accumulation but lack genetic tools, such as Rhodosporidium , Trichosporon , and Lipomyces . This review highlights recent accomplishments in metabolic engineering of oleaginous yeasts and recent advances in the development of genetic engineering tools in oleaginous yeasts within the last 3 years.

Application of metabolic engineering for the biotechnological production of L-valine.

PubMed

Oldiges, Marco; Eikmanns, Bernhard J; Blombach, Bastian

2014-07-01

The branched chain amino acid L-valine is an essential nutrient for higher organisms, such as animals and humans. Besides the pharmaceutical application in parenteral nutrition and as synthon for the chemical synthesis of e.g. herbicides or anti-viral drugs, L-valine is now emerging into the feed market, and significant increase of sales and world production is expected. In accordance, well-known microbial production bacteria, such as Escherichia coli and Corynebacterium glutamicum strains, have recently been metabolically engineered for efficient L-valine production under aerobic or anaerobic conditions, and the respective cultivation and production conditions have been optimized. This review summarizes the state of the art in L-valine biosynthesis and its regulation in E. coli and C. glutamicum with respect to optimal metabolic network for microbial L-valine production, genetic strain engineering and bioprocess development for L-valine production, and finally, it will shed light on emerging technologies that have the potential to accelerate strain and bioprocess engineering in the near future.

Comparative study of hop-containing products on human cytochrome p450-mediated metabolism.

PubMed

Foster, Brian C; Kearns, Nikia; Arnason, John T; Saleem, Ammar; Ogrodowczyk, Carolina; Desjardins, Suzanne

2009-06-10

Thirty-five national and international brands of beer were examined for their potential to affect human cytochrome P450 (CYP)-mediated metabolism. They represented the two main categories of beer, ales and lagers, and included a number of specialty products including bitter (porter, stout), coffee, ice, wheat, Pilsner, and hemp seed. Aliquots were examined for nonvolatile soluble solids, effect on CYP metabolism and P-glycoprotein (Pgp) transport, and major alpha- and beta-hop acids. Wide variance was detected in contents of alcohol, nonvolatile suspended solids, and hop acids and in the potential to affect CYP-mediated metabolism and Pgp-mediated efflux transport. Many of the products affected CYP2C9-mediated metabolism, and only two (NRP 306 and 307) markedly affected CYP3A4; hence, some products have the capacity to affect drug safety. CYP3A4, CYP3A5, CYP3A7, and CYP19 (aromatase) inhibition to the log concentration of beta-acid content was significant with r(2) > 0.37, suggesting that these components can account for some of the variation in inhibition of CYP metabolism.

Expanding Metabolic Engineering Algorithms Using Feasible Space and Shadow Price Constraint Modules

PubMed Central

Tervo, Christopher J.; Reed, Jennifer L.

2014-01-01

While numerous computational methods have been developed that use genome-scale models to propose mutants for the purpose of metabolic engineering, they generally compare mutants based on a single criteria (e.g., production rate at a mutant’s maximum growth rate). As such, these approaches remain limited in their ability to include multiple complex engineering constraints. To address this shortcoming, we have developed feasible space and shadow price constraint (FaceCon and ShadowCon) modules that can be added to existing mixed integer linear adaptive evolution metabolic engineering algorithms, such as OptKnock and OptORF. These modules allow strain designs to be identified amongst a set of multiple metabolic engineering algorithm solutions that are capable of high chemical production while also satisfying additional design criteria. We describe the various module implementations and their potential applications to the field of metabolic engineering. We then incorporated these modules into the OptORF metabolic engineering algorithm. Using an Escherichia coli genome-scale model (iJO1366), we generated different strain designs for the anaerobic production of ethanol from glucose, thus demonstrating the tractability and potential utility of these modules in metabolic engineering algorithms. PMID:25478320

Genome-scale metabolic modeling of responses to polymyxins in Pseudomonas aeruginosa.

PubMed

Zhu, Yan; Czauderna, Tobias; Zhao, Jinxin; Klapperstueck, Matthias; Maifiah, Mohd Hafidz Mahamad; Han, Mei-Ling; Lu, Jing; Sommer, Björn; Velkov, Tony; Lithgow, Trevor; Song, Jiangning; Schreiber, Falk; Li, Jian

2018-04-01

Pseudomonas aeruginosa often causes multidrug-resistant infections in immunocompromised patients, and polymyxins are often used as the last-line therapy. Alarmingly, resistance to polymyxins has been increasingly reported worldwide recently. To rescue this last-resort class of antibiotics, it is necessary to systematically understand how P. aeruginosa alters its metabolism in response to polymyxin treatment, thereby facilitating the development of effective therapies. To this end, a genome-scale metabolic model (GSMM) was used to analyze bacterial metabolic changes at the systems level. A high-quality GSMM iPAO1 was constructed for P. aeruginosa PAO1 for antimicrobial pharmacological research. Model iPAO1 encompasses an additional periplasmic compartment and contains 3022 metabolites, 4265 reactions, and 1458 genes in total. Growth prediction on 190 carbon and 95 nitrogen sources achieved an accuracy of 89.1%, outperforming all reported P. aeruginosa models. Notably, prediction of the essential genes for growth achieved a high accuracy of 87.9%. Metabolic simulation showed that lipid A modifications associated with polymyxin resistance exert a limited impact on bacterial growth and metabolism but remarkably change the physiochemical properties of the outer membrane. Modeling with transcriptomics constraints revealed a broad range of metabolic responses to polymyxin treatment, including reduced biomass synthesis, upregulated amino acid catabolism, induced flux through the tricarboxylic acid cycle, and increased redox turnover. Overall, iPAO1 represents the most comprehensive GSMM constructed to date for Pseudomonas. It provides a powerful systems pharmacology platform for the elucidation of complex killing mechanisms of antibiotics.

Production of anthocyanins in metabolically engineered microorganisms: Current status and perspectives.

PubMed

Zha, Jian; Koffas, Mattheos A G

2017-12-01

Microbial production of plant-derived natural products by engineered microorganisms has achieved great success thanks to large extend to metabolic engineering and synthetic biology. Anthocyanins, the water-soluble colored pigments found in terrestrial plants that are responsible for the red, blue and purple coloration of many flowers and fruits, are extensively used in food and cosmetics industry; however, their current supply heavily relies on complex extraction from plant-based materials. A promising alternative is their sustainable production in metabolically engineered microbes. Here, we review the recent progress on anthocyanin biosynthesis in engineered bacteria, with a special focus on the systematic engineering modifications such as selection and engineering of biosynthetic enzymes, engineering of transportation, regulation of UDP-glucose supply, as well as process optimization. These promising engineering strategies will facilitate successful microbial production of anthocyanins in industry in the near future.

Revising the Representation of Fatty Acid, Glycerolipid, and Glycerophospholipid Metabolism in the Consensus Model of Yeast Metabolism

PubMed Central

Aung, Hnin W.; Henry, Susan A.

2013-01-01

Abstract Genome-scale metabolic models are built using information from an organism's annotated genome and, correspondingly, information on reactions catalyzed by the set of metabolic enzymes encoded by the genome. These models have been successfully applied to guide metabolic engineering to increase production of metabolites of industrial interest. Congruity between simulated and experimental metabolic behavior is influenced by the accuracy of the representation of the metabolic network in the model. In the interest of applying the consensus model of Saccharomyces cerevisiae metabolism for increased productivity of triglycerides, we manually evaluated the representation of fatty acid, glycerophospholipid, and glycerolipid metabolism in the consensus model (Yeast v6.0). These areas of metabolism were chosen due to their tightly interconnected nature to triglyceride synthesis. Manual curation was facilitated by custom MATLAB functions that return information contained in the model for reactions associated with genes and metabolites within the stated areas of metabolism. Through manual curation, we have identified inconsistencies between information contained in the model and literature knowledge. These inconsistencies include incorrect gene-reaction associations, improper definition of substrates/products in reactions, inappropriate assignments of reaction directionality, nonfunctional β-oxidation pathways, and missing reactions relevant to the synthesis and degradation of triglycerides. Suggestions to amend these inconsistencies in the Yeast v6.0 model can be implemented through a MATLAB script provided in the Supplementary Materials, Supplementary Data S1 (Supplementary Data are available online at www.liebertpub.com/ind). PMID:24678285

Metabolic engineering of microorganisms for the production of higher alcohols.

PubMed

Choi, Yong Jun; Lee, Joungmin; Jang, Yu-Sin; Lee, Sang Yup

2014-09-02

Due to the increasing concerns about limited fossil resources and environmental problems, there has been much interest in developing biofuels from renewable biomass. Ethanol is currently used as a major biofuel, as it can be easily produced by existing fermentation technology, but it is not the best biofuel due to its low energy density, high vapor pressure, hygroscopy, and incompatibility with current infrastructure. Higher alcohols, including 1-propanol, 1-butanol, isobutanol, 2-methyl-1-butanol, and 3-methyl-1-butanol, which possess fuel properties more similar to those of petroleum-based fuel, have attracted particular interest as alternatives to ethanol. Since microorganisms isolated from nature do not allow production of these alcohols at high enough efficiencies, metabolic engineering has been employed to enhance their production. Here, we review recent advances in metabolic engineering of microorganisms for the production of higher alcohols. Copyright © 2014 Choi et al.

Metabolic Engineering of Microorganisms for the Production of Higher Alcohols

PubMed Central

Choi, Yong Jun; Lee, Joungmin; Jang, Yu-Sin

2014-01-01

ABSTRACT Due to the increasing concerns about limited fossil resources and environmental problems, there has been much interest in developing biofuels from renewable biomass. Ethanol is currently used as a major biofuel, as it can be easily produced by existing fermentation technology, but it is not the best biofuel due to its low energy density, high vapor pressure, hygroscopy, and incompatibility with current infrastructure. Higher alcohols, including 1-propanol, 1-butanol, isobutanol, 2-methyl-1-butanol, and 3-methyl-1-butanol, which possess fuel properties more similar to those of petroleum-based fuel, have attracted particular interest as alternatives to ethanol. Since microorganisms isolated from nature do not allow production of these alcohols at high enough efficiencies, metabolic engineering has been employed to enhance their production. Here, we review recent advances in metabolic engineering of microorganisms for the production of higher alcohols. PMID:25182323

Advances in metabolic engineering in the microbial production of fuels and chemicals from C1 gas.

PubMed

Humphreys, Christopher M; Minton, Nigel P

2018-04-01

The future sustainable production of chemicals and fuels from non-petrochemical sources, while at the same time reducing greenhouse gas (GHG) emissions, represent two of society's greatest challenges. Microbial chassis able to grow on waste carbon monoxide (CO) and carbon dioxide (CO 2 ) can provide solutions to both. Ranging from the anaerobic acetogens, through the aerobic chemoautotrophs to the photoautotrophic cyanobacteria, they are able to convert C1 gases into a range of chemicals and fuels which may be enhanced and extended through appropriate metabolic engineering. The necessary improvements will be facilitated by the increasingly sophisticated gene tools that are beginning to emerge as part of the Synthetic Biology revolution. These tools, in combination with more accurate metabolic and genome scale models, will enable C1 chassis to deliver their full potential. Copyright © 2017 The Authors. Published by Elsevier Ltd.. All rights reserved.

Tree morphologic plasticity explains deviation from metabolic scaling theory in semi-arid conifer forests, southwestern USA

Treesearch

Tyson L. Swetnam; Christopher D. O' Connor; Ann M. Lynch

2016-01-01

A significant concern about Metabolic Scaling Theory (MST) in real forests relates to consistent differences between the values of power law scaling exponents of tree primary size measures used to estimate mass and those predicted by MST. Here we consider why observed scaling exponents for diameter and height relationships deviate from MST predictions across...

Engineering electron metabolism to increase ethanol production in Clostridium thermocellum.

PubMed

Lo, Jonathan; Olson, Daniel G; Murphy, Sean Jean-Loup; Tian, Liang; Hon, Shuen; Lanahan, Anthony; Guss, Adam M; Lynd, Lee R

2017-01-01

The NfnAB (NADH-dependent reduced ferredoxin: NADP + oxidoreductase) and Rnf (ion-translocating reduced ferredoxin: NAD + oxidoreductase) complexes are thought to catalyze electron transfer between reduced ferredoxin and NAD(P) + . Efficient electron flux is critical for engineering fuel production pathways, but little is known about the relative importance of these enzymes in vivo. In this study we investigate the importance of the NfnAB and Rnf complexes in Clostridium thermocellum for growth on cellobiose and Avicel using gene deletion, enzyme assays, and fermentation product analysis. The NfnAB complex does not seem to play a major role in metabolism, since deletion of nfnAB genes had little effect on the distribution of fermentation products. By contrast, the Rnf complex appears to play an important role in ethanol formation. Deletion of rnf genes resulted in a decrease in ethanol formation. Overexpression of rnf genes resulted in an increase in ethanol production of about 30%, but only in strains where the hydG hydrogenase maturation gene was also deleted. Copyright © 2016 International Metabolic Engineering Society. All rights reserved.

Value of acid metabolic products in identification of certain corynebacteria.

PubMed Central

Reddy, C A; Kao, M

1978-01-01

Acid metabolic products of 23 strains of human and animal pathogenic corynebacteria, representing eight different species, were determined by gas chromatography. The results showed that the species examined were metabolically heterogeneous and could be presumptively identified based on the acid products produced. Corynebacterium equi did not produce any acids; C. renale produced lactate; and C. pyogenes produced major amounts of lactate, variable amounts of acetate, and minor amounts of succinate and pyruvate. C. kutscheri produced propionate and lactate as major products and pyruvate and oxalacetate as minor products. C. diphtheriae and C. pseudotuberculosis produced major amounts of propionate, acetate, and formate. In addition, C. pseudotuberculosis produced major amounts of pyruvate and minor amounts of succinate, lactate, and oxalacetate, whereas C. diphtheriae strains produced minor but variable amounts of lactate, succinate, fumarate, pyruvate, and oxalacetate. C. bovis produced aicd products similar to those of C. pyogenes but was readily distinguishable from the latter by the lack of hemolysis on blood agar, colony morphology, catalase reaction, and biochemicals. C. suis characteristically produced major amounts of ethanol, acetate, and formate and minor amounts of lactate and succinate but no propionate. PMID:96126

Response of white peach scale to metabolic stress disinfection and disinfestation (MSDD) treatment

USDA-ARS?s Scientific Manuscript database

Metabolic stress disinfection and disinfestation (MSDD) is a postharvest treatment that combines short periods of low pressure (vacuum) and high CO2 with ethanol vapor to control pathogens and arthropod pests on commodities. The system was tested against white peach scale, Pseudaulacaspis pentagona ...

Yeast cell metabolism investigated by CO{_2} production and soft X-ray irradiation

NASA Astrophysics Data System (ADS)

Masini, A.; Batani, D.; Previdi, F.; Milani, M.; Pozzi, A.; Turcu, E.; Huntington, S.; Takeyasu, H.

1999-01-01

Results obtained using a new technique for studying cell metabolism are presented. The technique, consisting in CO2 production monitoring, has been applied to Saccharomyces cerevisiae yeast cells. Also the cells were irradiated using the soft X-ray laser-plasma source at Rutherford Appleton Laboratory with the aim of producing a damage of metabolic processes at the wall level, responsible for fermentation, without great interference with respiration, taking place in mitochondria, and DNA activity. The source was calibrated with PIN diodes and X-ray spectrometers and used Teflon stripes as target, emitting X-rays at about 0.9 keV, with a very low penetration in biological material. X-ray doses delivered to the different cell compartments were calculated following a Lambert-Bouguet-Beer law. Immediately after irradiation, the damage to metabolic activity was measured again by monitoring CO2 production. Results showed a general reduction in gas production by irradiated samples, together with non-linear and non-monotone response to dose. There was also evidence of oscillations in cell metabolic activity and of X-ray induced changes in oscillation frequency.

Systems metabolic engineering strategies for the production of amino acids.

PubMed

Ma, Qian; Zhang, Quanwei; Xu, Qingyang; Zhang, Chenglin; Li, Yanjun; Fan, Xiaoguang; Xie, Xixian; Chen, Ning

2017-06-01

Systems metabolic engineering is a multidisciplinary area that integrates systems biology, synthetic biology and evolutionary engineering. It is an efficient approach for strain improvement and process optimization, and has been successfully applied in the microbial production of various chemicals including amino acids. In this review, systems metabolic engineering strategies including pathway-focused approaches, systems biology-based approaches, evolutionary approaches and their applications in two major amino acid producing microorganisms: Corynebacterium glutamicum and Escherichia coli, are summarized.

Why does offspring size affect performance? Integrating metabolic scaling with life-history theory

PubMed Central

Pettersen, Amanda K.; White, Craig R.; Marshall, Dustin J.

2015-01-01

Within species, larger offspring typically outperform smaller offspring. While the relationship between offspring size and performance is ubiquitous, the cause of this relationship remains elusive. By linking metabolic and life-history theory, we provide a general explanation for why larger offspring perform better than smaller offspring. Using high-throughput respirometry arrays, we link metabolic rate to offspring size in two species of marine bryozoan. We found that metabolism scales allometrically with offspring size in both species: while larger offspring use absolutely more energy than smaller offspring, larger offspring use proportionally less of their maternally derived energy throughout the dependent, non-feeding phase. The increased metabolic efficiency of larger offspring while dependent on maternal investment may explain offspring size effects—larger offspring reach nutritional independence (feed for themselves) with a higher proportion of energy relative to structure than smaller offspring. These findings offer a potentially universal explanation for why larger offspring tend to perform better than smaller offspring but studies on other taxa are needed. PMID:26559952

Genome-scale modelling of microbial metabolism with temporal and spatial resolution.

PubMed

Henson, Michael A

2015-12-01

Most natural microbial systems have evolved to function in environments with temporal and spatial variations. A major limitation to understanding such complex systems is the lack of mathematical modelling frameworks that connect the genomes of individual species and temporal and spatial variations in the environment to system behaviour. The goal of this review is to introduce the emerging field of spatiotemporal metabolic modelling based on genome-scale reconstructions of microbial metabolism. The extension of flux balance analysis (FBA) to account for both temporal and spatial variations in the environment is termed spatiotemporal FBA (SFBA). Following a brief overview of FBA and its established dynamic extension, the SFBA problem is introduced and recent progress is described. Three case studies are reviewed to illustrate the current state-of-the-art and possible future research directions are outlined. The author posits that SFBA is the next frontier for microbial metabolic modelling and a rapid increase in methods development and system applications is anticipated. © 2015 Authors; published by Portland Press Limited.

Systematic Construction of Kinetic Models from Genome-Scale Metabolic Networks

PubMed Central

Smallbone, Kieran; Klipp, Edda; Mendes, Pedro; Liebermeister, Wolfram

2013-01-01

The quantitative effects of environmental and genetic perturbations on metabolism can be studied in silico using kinetic models. We present a strategy for large-scale model construction based on a logical layering of data such as reaction fluxes, metabolite concentrations, and kinetic constants. The resulting models contain realistic standard rate laws and plausible parameters, adhere to the laws of thermodynamics, and reproduce a predefined steady state. These features have not been simultaneously achieved by previous workflows. We demonstrate the advantages and limitations of the workflow by translating the yeast consensus metabolic network into a kinetic model. Despite crudely selected data, the model shows realistic control behaviour, a stable dynamic, and realistic response to perturbations in extracellular glucose concentrations. The paper concludes by outlining how new data can continuously be fed into the workflow and how iterative model building can assist in directing experiments. PMID:24324546

Metabolic engineering of Zymomonas mobilis for 2,3-butanediol production from lignocellulosic biomass sugars

DOE PAGES

Yang, Shihui; Mohagheghi, Ali; Franden, Mary Ann; ...

2016-09-02

To develop pathways for advanced biofuel production, and to understand the impact of host metabolism and environmental conditions on heterologous pathway engineering for economic advanced biofuels production from biomass, we seek to redirect the carbon flow of the model ethanologen Zymomonas mobilis to produce desirable hydrocarbon intermediate 2,3-butanediol (2,3-BDO). 2,3-BDO is a bulk chemical building block, and can be upgraded in high yields to gasoline, diesel, and jet fuel. 2,3-BDO biosynthesis pathways from various bacterial species were examined, which include three genes encoding acetolactate synthase, acetolactate decarboxylase, and butanediol dehydrogenase. Bioinformatics analysis was carried out to pinpoint potential bottlenecks formore » high 2,3-BDO production. Different combinations of 2,3-BDO biosynthesis metabolic pathways using genes from different bacterial species have been constructed. Our results demonstrated that carbon flux can be deviated from ethanol production into 2,3-BDO biosynthesis, and all three heterologous genes are essential to efficiently redirect pyruvate from ethanol production for high 2,3-BDO production in Z. mobilis. The down-selection of best gene combinations up to now enabled Z. mobilis to reach the 2,3-BDO production of more than 10 g/L from glucose and xylose, as well as mixed C6/C5 sugar streams derived from the deacetylation and mechanical refining process. In conclusion, this study confirms the value of integrating bioinformatics analysis and systems biology data during metabolic engineering endeavors, provides guidance for value-added chemical production in Z. mobilis, and reveals the interactions between host metabolism, oxygen levels, and a heterologous 2,3-BDO biosynthesis pathway. Taken together, this work provides guidance for future metabolic engineering efforts aimed at boosting 2,3-BDO titer anaerobically.« less

Metabolic engineering of Zymomonas mobilis for 2,3-butanediol production from lignocellulosic biomass sugars

DOE Office of Scientific and Technical Information (OSTI.GOV)

Yang, Shihui; Mohagheghi, Ali; Franden, Mary Ann

To develop pathways for advanced biofuel production, and to understand the impact of host metabolism and environmental conditions on heterologous pathway engineering for economic advanced biofuels production from biomass, we seek to redirect the carbon flow of the model ethanologen Zymomonas mobilis to produce desirable hydrocarbon intermediate 2,3-butanediol (2,3-BDO). 2,3-BDO is a bulk chemical building block, and can be upgraded in high yields to gasoline, diesel, and jet fuel. 2,3-BDO biosynthesis pathways from various bacterial species were examined, which include three genes encoding acetolactate synthase, acetolactate decarboxylase, and butanediol dehydrogenase. Bioinformatics analysis was carried out to pinpoint potential bottlenecks formore » high 2,3-BDO production. Different combinations of 2,3-BDO biosynthesis metabolic pathways using genes from different bacterial species have been constructed. Our results demonstrated that carbon flux can be deviated from ethanol production into 2,3-BDO biosynthesis, and all three heterologous genes are essential to efficiently redirect pyruvate from ethanol production for high 2,3-BDO production in Z. mobilis. The down-selection of best gene combinations up to now enabled Z. mobilis to reach the 2,3-BDO production of more than 10 g/L from glucose and xylose, as well as mixed C6/C5 sugar streams derived from the deacetylation and mechanical refining process. In conclusion, this study confirms the value of integrating bioinformatics analysis and systems biology data during metabolic engineering endeavors, provides guidance for value-added chemical production in Z. mobilis, and reveals the interactions between host metabolism, oxygen levels, and a heterologous 2,3-BDO biosynthesis pathway. Taken together, this work provides guidance for future metabolic engineering efforts aimed at boosting 2,3-BDO titer anaerobically.« less

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

PubMed

Kim, Hyun Uk; Charusanti, Pep; Lee, Sang Yup; Weber, Tilmann

2016-08-27

Covering: 2012 to 2016Metabolic engineering using systems biology tools is increasingly applied to overproduce secondary metabolites for their potential industrial production. In this Highlight, recent relevant metabolic engineering studies are analyzed with emphasis on host selection and engineering approaches for the optimal production of various prokaryotic secondary metabolites: native versus heterologous hosts (e.g., Escherichia coli) and rational versus random approaches. This comparative analysis is followed by discussions on systems biology tools deployed in optimizing the production of secondary metabolites. The potential contributions of additional systems biology tools are also discussed in the context of current challenges encountered during optimization of secondary metabolite production.

[Effect of milk product with soy isoflavones on quality of life and bone metabolism in postmenopausal Spanish women: randomized trial].

PubMed

García-Martín, Antonia; Quesada Charneco, Miguel; Alvárez Guisado, Alejandro; Jiménez Moleón, José Juan; Fonollá Joya, Juristo; Muñoz-Torres, Manuel

2012-02-04

To analyze the effects of nutritional intervention with a milk product enriched with soy isoflavones on quality of life and bone metabolism in postmenopausal Spanish women. We performed a double-blind controlled randomized trial in ninety-nine postmenopausal women. Group S women (n=48) were randomized to consume milk product enriched with soy isoflavone (50 mg/day) while group C (n=51) consumed product control for 12 months. Parameters of quality of life (Cervantes scale), markers of bone metabolism and bone mass estimated by ultrasound of the calcaneus (QUS) were evaluated. Overall, there was an improvement in the domains menopause (P=.015) and vasomotor symptoms (P<.001). S group emphasized the assessment of vasomotor symptoms (P=.001) and differed positively from group C in health (P=.019), sex (P=.021) and partner (P=.002). Serum levels TRAP (P<.001) and OPG (P=.007) decreased and concentrations of 25-OH-vitamin D increased (P<.001) without differences between groups. In the assessment of QUS, there was an increase in estimated bone mineral density in group S (P=.040), whereas in group C there were no significant differences. Daily consumption of these milk products increases levels of 25-OH-vitamin D and decreases bone metabolism markers. Additional supplementation with soy isoflavones seems to improve quality of life and bone mass in Spanish postmenopausal women. Copyright © 2010 Elsevier España, S.L. All rights reserved.

Metabolic engineering in chemolithoautotrophic hosts for the production of fuels and chemicals.

PubMed

Nybo, S Eric; Khan, Nymul E; Woolston, Benjamin M; Curtis, Wayne R

2015-07-01

The ability of autotrophic organisms to fix CO2 presents an opportunity to utilize this 'greenhouse gas' as an inexpensive substrate for biochemical production. Unlike conventional heterotrophic microorganisms that consume carbohydrates and amino acids, prokaryotic chemolithoautotrophs have evolved the capacity to utilize reduced chemical compounds to fix CO2 and drive metabolic processes. The use of chemolithoautotrophic hosts as production platforms has been renewed by the prospect of metabolically engineered commodity chemicals and fuels. Efforts such as the ARPA-E electrofuels program highlight both the potential and obstacles that chemolithoautotrophic biosynthetic platforms provide. This review surveys the numerous advances that have been made in chemolithoautotrophic metabolic engineering with a focus on hydrogen oxidizing bacteria such as the model chemolithoautotrophic organism (Ralstonia), the purple photosynthetic bacteria (Rhodobacter), and anaerobic acetogens. Two alternative strategies of microbial chassis development are considered: (1) introducing or enhancing autotrophic capabilities (carbon fixation, hydrogen utilization) in model heterotrophic organisms, or (2) improving tools for pathway engineering (transformation methods, promoters, vectors etc.) in native autotrophic organisms. Unique characteristics of autotrophic growth as they relate to bioreactor design and process development are also discussed in the context of challenges and opportunities for genetic manipulation of organisms as production platforms. Copyright © 2015 International Metabolic Engineering Society. Published by Elsevier Inc. All rights reserved.

Recent trends in metabolic engineering of microorganisms for the production of advanced biofuels.

PubMed

Cheon, Seungwoo; Kim, Hye Mi; Gustavsson, Martin; Lee, Sang Yup

2016-12-01

As climate change has become one of the major global risks, our heavy dependence on petroleum-derived fuels has received much public attention. To solve such problems, production of sustainable fuels has been intensively studied over the past years. Thanks to recent advances in synthetic biology and metabolic engineering technologies, bio-based platforms for advanced biofuels production have been developed using various microorganisms. The strategies for production of advanced biofuels have converged upon four major metabolic routes: the 2-ketoacid pathway, the fatty acid synthesis (FAS) pathway, the isoprenoid pathway, and the reverse β-oxidation pathway. Additionally, the polyketide synthesis pathway has recently been attracting interest as a promising alternative biofuel production route. In this article, recent trends in advanced biofuels production are reviewed by categorizing them into three types of advanced biofuels: alcohols, biodiesel and jet fuel, and gasoline. Focus is given on the strategies of employing synthetic biology and metabolic engineering for the development of microbial strains producing advanced fuels. Finally, the prospects for future advances needed to achieve much more efficient bio-based production of advanced biofuels are discussed, focusing on designing advanced biofuel production pathways coupled with screening, modifying, and creating novel enzymes. Copyright © 2016 Elsevier Ltd. All rights reserved.

Both respiration and photosynthesis determine the scaling of plankton metabolism in the oligotrophic ocean

PubMed Central

Serret, Pablo; Robinson, Carol; Aranguren-Gassis, María; García-Martín, Enma Elena; Gist, Niki; Kitidis, Vassilis; Lozano, José; Stephens, John; Harris, Carolyn; Thomas, Rob

2015-01-01

Despite its importance to ocean–climate interactions, the metabolic state of the oligotrophic ocean has remained controversial for >15 years. Positions in the debate are that it is either hetero- or autotrophic, which suggests either substantial unaccounted for organic matter inputs, or that all available photosynthesis (P) estimations (including 14C) are biased. Here we show the existence of systematic differences in the metabolic state of the North (heterotrophic) and South (autotrophic) Atlantic oligotrophic gyres, resulting from differences in both P and respiration (R). The oligotrophic ocean is neither auto- nor heterotrophic, but functionally diverse. Our results show that the scaling of plankton metabolism by generalized P:R relationships that has sustained the debate is biased, and indicate that the variability of R, and not only of P, needs to be considered in regional estimations of the ocean's metabolic state. PMID:25908109

Central carbon metabolism influences cellulase production in Bacillus licheniformis.

PubMed

Wang, J; Liu, S; Li, Y; Wang, H; Xiao, S; Li, C; Liu, B

2018-01-01

Bacillus licheniformis that can produce cellulase including endo glucanase and glucosidase is an important industrial microbe for cellulose degradation. The purpose of this research was to assess the effect of endo glucanase gene bglC and glucosidase gene bglH on the central metabolic flux in B. licheniformis. bglC and bglH were knocked out using homologous recombination method, respectively, and the corresponding knockout strains were obtained for 13 C metabolic flux analysis. A significant change was observed in metabolic fluxes after 13 C metabolic flux ratio analysis. In both of the knockout strains, the increased fluxes of the pentose phosphate pathway and malic enzyme reaction enabled an elevated supply of NADPH which provided enough reducing power for the in vivo synthesis reactions. The fluxes through tricarboxylic acid cycle and anaplerotic reactions increased fast in the two knockout strains, which meant more energy generated. The changed fluxes in central carbon metabolism provided a holistic view of the physiological status in B. licheniformis and possible targets for further strain engineering. Cellulase is very important in the field of agriculture and bioenergy because of its degrading effect on cellulosic biomass. This study presented the effect of central carbon metabolism on cellulase production in Bacillus licheniformis. The study also provided a holistic view of the physiological status in B. licheniformis. The shifted metabolism provided a quantitative evaluation of the biosynthesis of cellulase and a priority ranked target list for further strain engineering. © 2017 The Society for Applied Microbiology.

Combining Flux Balance and Energy Balance Analysis for Large-Scale Metabolic Network: Biochemical Circuit Theory for Analysis of Large-Scale Metabolic Networks

NASA Technical Reports Server (NTRS)

Beard, Daniel A.; Liang, Shou-Dan; Qian, Hong; Biegel, Bryan (Technical Monitor)

2001-01-01

Predicting behavior of large-scale biochemical metabolic networks represents one of the greatest challenges of bioinformatics and computational biology. Approaches, such as flux balance analysis (FBA), that account for the known stoichiometry of the reaction network while avoiding implementation of detailed reaction kinetics are perhaps the most promising tools for the analysis of large complex networks. As a step towards building a complete theory of biochemical circuit analysis, we introduce energy balance analysis (EBA), which compliments the FBA approach by introducing fundamental constraints based on the first and second laws of thermodynamics. Fluxes obtained with EBA are thermodynamically feasible and provide valuable insight into the activation and suppression of biochemical pathways.

Metabolic rate and environmental productivity: Well-provisioned animals evolved to run and idle fast

PubMed Central

Mueller, Pamela; Diamond, Jared

2001-01-01

Even among vertebrate species of the same body mass and higher-level taxonomic group, metabolic rates exhibit substantial differences, for which diverse explanatory factors—such as dietary energy content, latitude, altitude, temperature, and rainfall—have been postulated. A unifying underlying factor could be food availability, in turn controlled by net primary productivity (NPP) of the animal's natural environment. We tested this possibility by studying five North American species of Peromyscus mice, all of them similar in diet (generalist omnivores) and in gut morphology but differing by factors of up to 13 in NPP of their habitat of origin. We maintained breeding colonies of all five species in the laboratory under identical conditions and consuming identical diets. Basal metabolic rate (BMR) and daily ad libitum food intake both increased with NPP, which explained 88% and 90% of their variances, respectively. High-metabolism mouse species from high-NPP environments were behaviorally more active than were low-metabolism species from low-NPP environments. Intestinal glucose uptake capacity also increased with NPP (and with BMR and food intake), because species of high-NPP environments had larger small intestines and higher uptake rates. For metabolic rates of our five species, the driving environmental variable is environmental productivity itself (and hence food availability), rather than temporal variability of productivity. Thus, species that have evolved in the presence of abundant food run their metabolism “fast,” both while active and while idling, as compared with species of less productive environments, even when all species are given access to unlimited food. PMID:11606744

A metabolic basis for impaired muscle force production and neuromuscular compensation during sprint cycling.

PubMed

Bundle, Matthew W; Ernst, Carrie L; Bellizzi, Matthew J; Wright, Seth; Weyand, Peter G

2006-11-01

For both different individuals and modes of locomotion, the external forces determining all-out sprinting performances fall predictably with effort duration from the burst maximums attained for 3 s to those that can be supported aerobically as trial durations extend to roughly 300 s. The common time course of this relationship suggests a metabolic basis for the decrements in the force applied to the environment. However, the mechanical and neuromuscular responses to impaired force production (i.e., muscle fatigue) are generally considered in relation to fractions of the maximum force available, or the maximum voluntary contraction (MVC). We hypothesized that these duration-dependent decrements in external force application result from a reliance on anaerobic metabolism for force production rather than the absolute force produced. We tested this idea by examining neuromuscular activity during two modes of sprint cycling with similar external force requirements but differing aerobic and anaerobic contributions to force production: one- and two-legged cycling. In agreement with previous studies, we found greater peak per leg aerobic metabolic rates [59% (+/-6 SD)] and pedal forces at VO2 peak [30% (+/-9)] during one- vs. two-legged cycling. We also determined downstroke pedal forces and neuromuscular activity by surface electromyography during 15 to 19 all-out constant load sprints lasting from 12 to 400 s for both modes of cycling. In support of our hypothesis, we found that the greater reliance on anaerobic metabolism for force production induced compensatory muscle recruitment at lower pedal forces during two- vs. one-legged sprint cycling. We conclude that impaired muscle force production and compensatory neuromuscular activity during sprinting are triggered by a reliance on anaerobic metabolism for force production.

Metabolic flux profiling of MDCK cells during growth and canine adenovirus vector production.

PubMed

Carinhas, Nuno; Pais, Daniel A M; Koshkin, Alexey; Fernandes, Paulo; Coroadinha, Ana S; Carrondo, Manuel J T; Alves, Paula M; Teixeira, Ana P

2016-03-23

Canine adenovirus vector type 2 (CAV2) represents an alternative to human adenovirus vectors for certain gene therapy applications, particularly neurodegenerative diseases. However, more efficient production processes, assisted by a greater understanding of the effect of infection on producer cells, are required. Combining [1,2-(13)C]glucose and [U-(13)C]glutamine, we apply for the first time (13)C-Metabolic flux analysis ((13)C-MFA) to study E1-transformed Madin-Darby Canine Kidney (MDCK) cells metabolism during growth and CAV2 production. MDCK cells displayed a marked glycolytic and ammoniagenic metabolism, and (13)C data revealed a large fraction of glutamine-derived labelling in TCA cycle intermediates, emphasizing the role of glutamine anaplerosis. (13)C-MFA demonstrated the importance of pyruvate cycling in balancing glycolytic and TCA cycle activities, as well as occurrence of reductive alphaketoglutarate (AKG) carboxylation. By turn, CAV2 infection significantly upregulated fluxes through most central metabolism, including glycolysis, pentose-phosphate pathway, glutamine anaplerosis and, more prominently, reductive AKG carboxylation and cytosolic acetyl-coenzyme A formation, suggestive of increased lipogenesis. Based on these results, we suggest culture supplementation strategies to stimulate nucleic acid and lipid biosynthesis for improved canine adenoviral vector production.

Toward Systems Metabolic Engineering of Streptomycetes for Secondary Metabolites Production.

PubMed

Robertsen, Helene Lunde; Weber, Tilmann; Kim, Hyun Uk; Lee, Sang Yup

2018-01-01

Streptomycetes are known for their inherent ability to produce pharmaceutically relevant secondary metabolites. Discovery of medically useful, yet novel compounds has become a great challenge due to frequent rediscovery of known compounds and a consequent decline in the number of relevant clinical trials in the last decades. A paradigm shift took place when the first whole genome sequences of streptomycetes became available, from which silent or "cryptic" biosynthetic gene clusters (BGCs) were discovered. Cryptic BGCs reveal a so far untapped potential of the microorganisms for the production of novel compounds, which has spurred new efforts in understanding the complex regulation between primary and secondary metabolism. This new trend has been accompanied with development of new computational resources (genome and compound mining tools), generation of various high-quality omics data, establishment of molecular tools, and other strain engineering strategies. They all come together to enable systems metabolic engineering of streptomycetes, allowing more systematic and efficient strain development. In this review, the authors present recent progresses within systems metabolic engineering of streptomycetes for uncovering their hidden potential to produce novel compounds and for the improved production of secondary metabolites. © 2017 WILEY-VCH Verlag GmbH & Co. KGaA, Weinheim.

Lipid metabolism and potentials of biofuel and high added-value oil production in red algae.

PubMed

Sato, Naoki; Moriyama, Takashi; Mori, Natsumi; Toyoshima, Masakazu

2017-04-01

Biomass production is currently explored in microalgae, macroalgae and land plants. Microalgal biofuel development has been performed mostly in green algae. In the Japanese tradition, macrophytic red algae such as Pyropia yezoensis and Gelidium crinale have been utilized as food and industrial materials. Researches on the utilization of unicellular red microalgae such as Cyanidioschyzon merolae and Porphyridium purpureum started only quite recently. Red algae have relatively large plastid genomes harboring more than 200 protein-coding genes that support the biosynthetic capacity of the plastid. Engineering the plastid genome is a unique potential of red microalgae. In addition, large-scale growth facilities of P. purpureum have been developed for industrial production of biofuels. C. merolae has been studied as a model alga for cell and molecular biological analyses with its completely determined genomes and transformation techniques. Its acidic and warm habitat makes it easy to grow this alga axenically in large scales. Its potential as a biofuel producer is recently documented under nitrogen-limited conditions. Metabolic pathways of the accumulation of starch and triacylglycerol and the enzymes involved therein are being elucidated. Engineering these regulatory mechanisms will open a possibility of exploiting the full capability of production of biofuel and high added-value oil. In the present review, we will describe the characteristics and potential of these algae as biotechnological seeds.

Improving lactate metabolism in an intensified CHO culture process: productivity and product quality considerations.

PubMed

Xu, Sen; Hoshan, Linda; Chen, Hao

2016-11-01

In this study, we discussed the development and optimization of an intensified CHO culture process, highlighting medium and control strategies to improve lactate metabolism. A few strategies, including supplementing glucose with other sugars (fructose, maltose, and galactose), controlling glucose level at <0.2 mM, and supplementing medium with copper sulfate, were found to be effective in reducing lactate accumulation. Among them, copper sulfate supplementation was found to be critical for process optimization when glucose was in excess. When copper sulfate was supplemented in the new process, two-fold increase in cell density (66.5 ± 8.4 × 10(6) cells/mL) and titer (11.9 ± 0.6 g/L) was achieved. Productivity and product quality attributes differences between batch, fed-batch, and concentrated fed-batch cultures were discussed. The importance of process and cell metabolism understanding when adapting the existing process to a new operational mode was demonstrated in the study.

LRP in amyloid-beta production and metabolism.

PubMed

Bu, Guojun; Cam, Judy; Zerbinatti, Celina

2006-11-01

Amyloid-beta peptide (Abeta) production and accumulation in the brain is a central event in the pathogenesis of Alzheimer's disease (AD). Recent studies have shown that apolipoprotein E (apoE) receptors, members of the low-density lipoprotein receptor (LDLR) family, modulate Abeta production as well as Abeta cellular uptake. Abeta is derived from proteolytic processing of the amyloid precursor protein (APP), which interacts with several members of the LDLR family. Studies from our laboratory have focused on two members of the LDLR family, the LDLR-related protein (LRP) and LRP1B. Our in vitro studies have shown that while LRP's rapid endocytosis facilitates APP endocytic trafficking and processing to Abeta, LRP1B's slow endocytosis inhibits these processes. In addition to modulating APP endocytic trafficking, LRP's rapid endocytosis also facilitates Abeta cellular uptake by binding to Abeta either directly or via LRP ligands such as apoE. Our in vivo studies using transgenic mice have shown that overexpression of LRP in central nervous system (CNS) neurons increases soluble brain Abeta and this increase correlates with deficits in memory. Together our studies demonstrate that members of the LDLR family modulate APP processing and Abeta metabolism by several independent mechanisms. Understanding the pathways that modulate brain Abeta metabolism may enable the rational design of molecular medicine to treat AD.

Metabolic engineering of E. coli top 10 for production of vanillin through FA catabolic pathway and bioprocess optimization using RSM.

PubMed

Chakraborty, Debkumar; Gupta, Gaganjot; Kaur, Baljinder

2016-12-01

Metabolic engineering and construction of recombinant Escherichia coli strains carrying feruloyl-CoA synthetase and enoyl-CoA hydratase genes for the bioconversion of ferulic acid to vanillin offers an alternative way to produce vanillin. Isolation and designing of fcs and ech genes was carried out using computer assisted protocol and the designed vanillin biosynthetic gene cassette was cloned in pCCIBAC expression vector for introduction in E. coli top 10. Recombinant strain was implemented for the statistical optimization of process parameters influencing F A to vanillin biotransformation. CCD matrix constituted of process variables like FA concentration, time, temperature and biomass with intracellular, extracellular and total vanillin productions as responses. Production was scaled up and 68 mg/L of vanillin was recovered from 10 mg/L of FA using cell extracts from 1 mg biomass within 30 min. Kinetic activity of enzymes were characterized. From LCMS-ESI analysis a metabolic pathway of FA degradation and vanillin production was predicted. Copyright © 2016 Elsevier Inc. All rights reserved.

Engineering plant metabolism into microbes: from systems biology to synthetic biology.

PubMed

Xu, Peng; Bhan, Namita; Koffas, Mattheos A G

2013-04-01

Plant metabolism represents an enormous repository of compounds that are of pharmaceutical and biotechnological importance. Engineering plant metabolism into microbes will provide sustainable solutions to produce pharmaceutical and fuel molecules that could one day replace substantial portions of the current fossil-fuel based economy. Metabolic engineering entails targeted manipulation of biosynthetic pathways to maximize yields of desired products. Recent advances in Systems Biology and the emergence of Synthetic Biology have accelerated our ability to design, construct and optimize cell factories for metabolic engineering applications. Progress in predicting and modeling genome-scale metabolic networks, versatile gene assembly platforms and delicate synthetic pathway optimization strategies has provided us exciting opportunities to exploit the full potential of cell metabolism. In this review, we will discuss how systems and synthetic biology tools can be integrated to create tailor-made cell factories for efficient production of natural products and fuel molecules in microorganisms. Copyright © 2012 Elsevier Ltd. All rights reserved.

Enhanced d-lactic acid production by recombinant Saccharomyces cerevisiae following optimization of the global metabolic pathway.

PubMed

Yamada, Ryosuke; Wakita, Kazuki; Mitsui, Ryosuke; Ogino, Hiroyasu

2017-09-01

Utilization of renewable feedstocks for the production of bio-based chemicals such as d-lactic acid by engineering metabolic pathways in the yeast Saccharomyces cerevisiae has recently become an attractive option. In this study, to realize efficient d-lactic acid production by S. cerevisiae, the expression of 12 glycolysis-related genes and the Leuconostoc mesenteroides d-LDH gene was optimized using a previously developed global metabolic engineering strategy, and repeated batch fermentation was carried out using the resultant strain YPH499/dPdA3-34/DLDH/1-18. Stable d-lactic acid production through 10 repeated batch fermentations was achieved using YPH499/dPdA3-34/DLDH/1-18. The average d-lactic acid production, productivity, and yield with 10 repeated batch fermentations were 60.3 g/L, 2.80 g/L/h, and 0.646, respectively. The present study is the first report of the application of a global metabolic engineering strategy for bio-based chemical production, and it shows the potential for efficient production of such chemicals by global metabolic engineering of the yeast S. cerevisiae. Biotechnol. Bioeng. 2017;114: 2075-2084. © 2017 Wiley Periodicals, Inc. © 2017 Wiley Periodicals, Inc.

Respiratory glycerol metabolism of Actinobacillus succinogenes 130Z for succinate production.

PubMed

Schindler, Bryan D; Joshi, Rajasi V; Vieille, Claire

2014-09-01

Actinobacillus succinogenes 130Z naturally produces among the highest levels of succinate from a variety of inexpensive carbon substrates. A few studies have demonstrated that A. succinogenes can anaerobically metabolize glycerol, a waste product of biodiesel manufacture and an inexpensive feedstock, to produce high yields of succinate. However, all these studies were performed in the presence of yeast extract, which largely removes the redox constraints associated with fermenting glycerol, a highly reduced molecule. We demonstrated that A. succinogenes cannot ferment glycerol in minimal medium, but that it can metabolize glycerol by aerobic or anaerobic respiration. These results were expected based on the A. succinogenes genome, which encodes respiratory enzymes, but no pathway for 1,3-propanediol production. We investigated A. succinogenes's glycerol metabolism in minimal medium in a variety of respiratory conditions by comparing growth, metabolite production, and in vitro activity of terminal oxidoreductases. Nitrate inhibited succinate production by inhibiting fumarate reductase expression. In contrast, growth in the presence of dimethylsulfoxide and in microaerobic conditions allowed high succinate yields. The highest succinate yield was 0.75 mol/mol glycerol (75 % of the maximum theoretical yield) in continuous microaerobic cultures. A. succinogenes could also grow and produce succinate on partially refined glycerols obtained directly from biodiesel manufacture. Finally, by expressing a heterologous 1,3-propanediol synthesis pathway in A. succinogenes, we provide the first proof of concept that A. succinogenes can be engineered to grow fermentatively on glycerol.

Traceability, reproducibility and wiki-exploration for “à-la-carte” reconstructions of genome-scale metabolic models

PubMed Central

Got, Jeanne; Cortés, María Paz; Maass, Alejandro

2018-01-01

Genome-scale metabolic models have become the tool of choice for the global analysis of microorganism metabolism, and their reconstruction has attained high standards of quality and reliability. Improvements in this area have been accompanied by the development of some major platforms and databases, and an explosion of individual bioinformatics methods. Consequently, many recent models result from “à la carte” pipelines, combining the use of platforms, individual tools and biological expertise to enhance the quality of the reconstruction. Although very useful, introducing heterogeneous tools, that hardly interact with each other, causes loss of traceability and reproducibility in the reconstruction process. This represents a real obstacle, especially when considering less studied species whose metabolic reconstruction can greatly benefit from the comparison to good quality models of related organisms. This work proposes an adaptable workspace, AuReMe, for sustainable reconstructions or improvements of genome-scale metabolic models involving personalized pipelines. At each step, relevant information related to the modifications brought to the model by a method is stored. This ensures that the process is reproducible and documented regardless of the combination of tools used. Additionally, the workspace establishes a way to browse metabolic models and their metadata through the automatic generation of ad-hoc local wikis dedicated to monitoring and facilitating the process of reconstruction. AuReMe supports exploration and semantic query based on RDF databases. We illustrate how this workspace allowed handling, in an integrated way, the metabolic reconstructions of non-model organisms such as an extremophile bacterium or eukaryote algae. Among relevant applications, the latter reconstruction led to putative evolutionary insights of a metabolic pathway. PMID:29791443

Regulation of metabolic products and gene expression in Fusarium asiaticum by agmatine addition.

PubMed

Suzuki, Tadahiro; Kim, Young-Kyung; Yoshioka, Hifumi; Iwahashi, Yumiko

2013-05-01

The metabolic products resulting from the cultivation of F. asiaticum in agmatine were identified using capillary electrophoresis-time of flight mass spectrometry. Glyoxylic acid was detected from fungal cultures grown in agmatine, while it was absent in control cells. The abundance of other metabolic products of the glycolytic pathway also increased because of agmatine; however, there was no increase in the amounts of pyruvic acid or metabolites from the tricarboxylic acid cycle. Moreover, gene expression levels within Fusarium asiaticum exposed to agmatine were analyzed by DNA microarray. Changes in gene expression levels directed the changes in metabolic products. Our results suggest that acetyl coenzyme A, which is a starting substrate for the biosynthesis of deoxynivalenol (DON), was simultaneously produced by activated β-oxidation. Furthermore, the content of 4-aminobutyrate (GABA) was increased in the agmatine addition culture medium. GABA can be synthesized from agmatine through putrescine and might influence the regulation of DON-related genes.

Impact of synthetic biology and metabolic engineering on industrial production of fine chemicals.

PubMed

Jullesson, David; David, Florian; Pfleger, Brian; Nielsen, Jens

2015-11-15

Industrial bio-processes for fine chemical production are increasingly relying on cell factories developed through metabolic engineering and synthetic biology. The use of high throughput techniques and automation for the design of cell factories, and especially platform strains, has played an important role in the transition from laboratory research to industrial production. Model organisms such as Saccharomyces cerevisiae and Escherichia coli remain widely used host strains for industrial production due to their robust and desirable traits. This review describes some of the bio-based fine chemicals that have reached the market, key metabolic engineering tools that have allowed this to happen and some of the companies that are currently utilizing these technologies for developing industrial production processes. Copyright © 2015 Elsevier Inc. All rights reserved.

Fructose increases corticosterone production in association with NADPH metabolism alterations in rat epididymal white adipose tissue.

PubMed

Prince, Paula D; Santander, Yanina A; Gerez, Estefania M; Höcht, Christian; Polizio, Ariel H; Mayer, Marcos A; Taira, Carlos A; Fraga, Cesar G; Galleano, Monica; Carranza, Andrea

2017-08-01

Metabolic syndrome is an array of closely metabolic disorders that includes glucose intolerance/insulin resistance, central obesity, dyslipidemia, and hypertension. Fructose, a highly lipogenic sugar, has profound metabolic effects in adipose tissue, and has been associated with the etiopathology of many components of the metabolic syndrome. In adipocytes, the enzyme 11 β-HSD1 amplifies local glucocorticoid production, being a key player in the pathogenesis of central obesity and metabolic syndrome. 11 β-HSD1 reductase activity is dependent on NADPH, a cofactor generated by H6PD inside the endoplasmic reticulum. Our focus was to explore the effect of fructose overload on epididymal white adipose tissue (EWAT) machinery involved in glucocorticoid production and NADPH and oxidants metabolism. Male Sprague-Dawley rats fed with a fructose solution (10% (w/v) in tap water) during 9 weeks developed some characteristic features of metabolic syndrome, such as hypertriglyceridemia, and hypertension. In addition, high levels of plasma and EWAT corticosterone were detected. Activities and expressions of H6PD and 11 β-HSD1, NAPDH content, superoxide anion production, expression of NADPH oxidase 2 subunits, and indicators of oxidative metabolism were measured. Fructose overloaded rats showed an increased potential in oxidant production respect to control rats. In parallel, in EWAT from fructose overloaded rats we found higher expression/activity of H6PD and 11 β-HSD1, and NADPH/NADP + ratio. Our in vivo results support that fructose overload installs in EWAT conditions favoring glucocorticoid production through higher H6PD expression/activity supplying NADPH for enhanced 11 β-HSD1 expression/activity, becoming this tissue a potential extra-adrenal source of corticosterone under these experimental conditions. Copyright © 2017 Elsevier Inc. All rights reserved.

Global metabolic interaction network of the human gut microbiota for context-specific community-scale analysis

PubMed Central

Sung, Jaeyun; Kim, Seunghyeon; Cabatbat, Josephine Jill T.; Jang, Sungho; Jin, Yong-Su; Jung, Gyoo Yeol; Chia, Nicholas; Kim, Pan-Jun

2017-01-01

A system-level framework of complex microbe–microbe and host–microbe chemical cross-talk would help elucidate the role of our gut microbiota in health and disease. Here we report a literature-curated interspecies network of the human gut microbiota, called NJS16. This is an extensive data resource composed of ∼570 microbial species and 3 human cell types metabolically interacting through >4,400 small-molecule transport and macromolecule degradation events. Based on the contents of our network, we develop a mathematical approach to elucidate representative microbial and metabolic features of the gut microbial community in a given population, such as a disease cohort. Applying this strategy to microbiome data from type 2 diabetes patients reveals a context-specific infrastructure of the gut microbial ecosystem, core microbial entities with large metabolic influence, and frequently produced metabolic compounds that might indicate relevant community metabolic processes. Our network presents a foundation towards integrative investigations of community-scale microbial activities within the human gut. PMID:28585563

Global metabolic interaction network of the human gut microbiota for context-specific community-scale analysis.

PubMed

Sung, Jaeyun; Kim, Seunghyeon; Cabatbat, Josephine Jill T; Jang, Sungho; Jin, Yong-Su; Jung, Gyoo Yeol; Chia, Nicholas; Kim, Pan-Jun

2017-06-06

A system-level framework of complex microbe-microbe and host-microbe chemical cross-talk would help elucidate the role of our gut microbiota in health and disease. Here we report a literature-curated interspecies network of the human gut microbiota, called NJS16. This is an extensive data resource composed of ∼570 microbial species and 3 human cell types metabolically interacting through >4,400 small-molecule transport and macromolecule degradation events. Based on the contents of our network, we develop a mathematical approach to elucidate representative microbial and metabolic features of the gut microbial community in a given population, such as a disease cohort. Applying this strategy to microbiome data from type 2 diabetes patients reveals a context-specific infrastructure of the gut microbial ecosystem, core microbial entities with large metabolic influence, and frequently produced metabolic compounds that might indicate relevant community metabolic processes. Our network presents a foundation towards integrative investigations of community-scale microbial activities within the human gut.

Metabolic engineering of Clostridium acetobutylicum for enhanced production of butyric acid.

PubMed

Jang, Yu-Sin; Woo, Hee Moon; Im, Jung Ae; Kim, In Ho; Lee, Sang Yup

2013-11-01

Clostridium acetobutylicum has been considered as an attractive platform host for biorefinery due to its metabolic diversity. Considering its capability to overproduce butanol through butyrate, it was thought that butyric acid can also be efficiently produced by this bacterium through metabolic engineering. The pta-ctfB-deficient C. acetobutylicum CEKW, in which genes encoding phosphotransacetylase and CoA-transferase were knocked out, was assessed for its potential as a butyric acid producer in fermentations with four controlled pH values at 5.0, 5.5, 6.0, and 6.4. Butyric acid could be best produced by fermentation of the CEKW at pH 6.0, resulting in the highest titer of 26.6 g/l, which is 6.4 times higher than that obtained with the wild type. However, due to the remaining solventogenic ability of the CEKW, 3.6 g/l solvents were also produced. Thus, the CEKW was further engineered by knocking out the adhE1-encoding aldehyde/alcohol dehydrogenase to prevent solvent production. Batch fermentation of the resulting C. acetobutylicum HCEKW at pH 6.0 showed increased butyric acid production to 30.8 g/l with a ratio of butyric-to-acetic acid (BA/AA) of 6.6 g/g and a productivity of 0.72 g/l/h from 86.9 g/l glucose, while negligible solvent (0.8 g/l ethanol only) was produced. The butyric acid titer, BA/AA ratio, and productivity obtained in this study were the highest values reported for C. acetobutylicum, and the BA/AA ratio and productivity were also comparable to those of native butyric acid producer Clostridium tyrobutyricum. These results suggested that the simultaneous deletion of the pta-ctfB-adhE1 in C. acetobutylicum resulted in metabolic switch from biphasic to acidogenic fermentation, which enhanced butyric acid production.

Genome-scale biological models for industrial microbial systems.

PubMed

Xu, Nan; Ye, Chao; Liu, Liming

2018-04-01

The primary aims and challenges associated with microbial fermentation include achieving faster cell growth, higher productivity, and more robust production processes. Genome-scale biological models, predicting the formation of an interaction among genetic materials, enzymes, and metabolites, constitute a systematic and comprehensive platform to analyze and optimize the microbial growth and production of biological products. Genome-scale biological models can help optimize microbial growth-associated traits by simulating biomass formation, predicting growth rates, and identifying the requirements for cell growth. With regard to microbial product biosynthesis, genome-scale biological models can be used to design product biosynthetic pathways, accelerate production efficiency, and reduce metabolic side effects, leading to improved production performance. The present review discusses the development of microbial genome-scale biological models since their emergence and emphasizes their pertinent application in improving industrial microbial fermentation of biological products.

Finding elementary flux modes in metabolic networks based on flux balance analysis and flux coupling analysis: application to the analysis of Escherichia coli metabolism.

PubMed

Tabe-Bordbar, Shayan; Marashi, Sayed-Amir

2013-12-01

Elementary modes (EMs) are steady-state metabolic flux vectors with minimal set of active reactions. Each EM corresponds to a metabolic pathway. Therefore, studying EMs is helpful for analyzing the production of biotechnologically important metabolites. However, memory requirements for computing EMs may hamper their applicability as, in most genome-scale metabolic models, no EM can be computed due to running out of memory. In this study, we present a method for computing randomly sampled EMs. In this approach, a network reduction algorithm is used for EM computation, which is based on flux balance-based methods. We show that this approach can be used to recover the EMs in the medium- and genome-scale metabolic network models, while the EMs are sampled in an unbiased way. The applicability of such results is shown by computing “estimated” control-effective flux values in Escherichia coli metabolic network.

Metabolic pathways recruited in the production of a recombinant enveloped virus: mining targets for process and cell engineering.

PubMed

Rodrigues, A F; Formas-Oliveira, A S; Bandeira, V S; Alves, P M; Hu, W S; Coroadinha, A S

2013-11-01

Biopharmaceuticals derived from enveloped virus comprise an expanding market of vaccines, oncolytic vectors and gene therapy products. Thus, increased attention is given to the development of robust high-titer cell hosts for their manufacture. However, the knowledge on the physiological constraints modulating virus production is still scarce and the use of integrated strategies to improve hosts productivity and upstream bioprocess an under-explored territory. In this work, we conducted a functional genomics study, including the transcriptional profiling and central carbon metabolism analysis, following the metabolic changes in the transition 'parental-to-producer' of two human cell lines producing recombinant retrovirus. Results were gathered into three comprehensive metabolic maps, providing a broad and integrated overview of gene expression changes for both cell lines. Eight pathways were identified to be recruited in the virus production state: amino acid catabolism, carbohydrate catabolism and integration of the energy metabolism, nucleotide metabolism, glutathione metabolism, pentose phosphate pathway, polyamines biosynthesis and lipid metabolism. Their ability to modulate viral titers was experimentally challenged, leading to improved specific productivities of recombinant retrovirus up to 6-fold. Within recruited pathways in the virus production state, we sought for metabolic engineering gene targets in the low producing phenotypes. A mining strategy was used alternative to the traditional approach 'high vs. low producer' clonal comparison. Instead, 'high vs. low producer' from different genetic backgrounds (i.e. cell origins) were compared. Several genes were identified as limiting in the low-production phenotype, including two enzymes from cholesterol biosynthesis, two enzymes from glutathione biosynthesis and the regulatory machinery of polyamines biosynthesis. This is thus a frontier work, bridging fundamentals to technological research and contributing

The JBEI quantitative metabolic modeling library (jQMM): a python library for modeling microbial metabolism.

PubMed

Birkel, Garrett W; Ghosh, Amit; Kumar, Vinay S; Weaver, Daniel; Ando, David; Backman, Tyler W H; Arkin, Adam P; Keasling, Jay D; Martín, Héctor García

2017-04-05

Modeling of microbial metabolism is a topic of growing importance in biotechnology. Mathematical modeling helps provide a mechanistic understanding for the studied process, separating the main drivers from the circumstantial ones, bounding the outcomes of experiments and guiding engineering approaches. Among different modeling schemes, the quantification of intracellular metabolic fluxes (i.e. the rate of each reaction in cellular metabolism) is of particular interest for metabolic engineering because it describes how carbon and energy flow throughout the cell. In addition to flux analysis, new methods for the effective use of the ever more readily available and abundant -omics data (i.e. transcriptomics, proteomics and metabolomics) are urgently needed. The jQMM library presented here provides an open-source, Python-based framework for modeling internal metabolic fluxes and leveraging other -omics data for the scientific study of cellular metabolism and bioengineering purposes. Firstly, it presents a complete toolbox for simultaneously performing two different types of flux analysis that are typically disjoint: Flux Balance Analysis and 13 C Metabolic Flux Analysis. Moreover, it introduces the capability to use 13 C labeling experimental data to constrain comprehensive genome-scale models through a technique called two-scale 13 C Metabolic Flux Analysis (2S- 13 C MFA). In addition, the library includes a demonstration of a method that uses proteomics data to produce actionable insights to increase biofuel production. Finally, the use of the jQMM library is illustrated through the addition of several Jupyter notebook demonstration files that enhance reproducibility and provide the capability to be adapted to the user's specific needs. jQMM will facilitate the design and metabolic engineering of organisms for biofuels and other chemicals, as well as investigations of cellular metabolism and leveraging -omics data. As an open source software project, we hope it will

Metabolic differences in cattle with excitable temperaments can influence productivity

USDA-ARS?s Scientific Manuscript database

Temperament can negatively affect various production traits, including live weight, ADG, DMI, conception rates, and carcass weight. Three research studies are summarized which indicate the potential influence of temperament on metabolism. In Brahman heifers, (n=12) the 6 most temperamental and 6 mos...

Progress of succinic acid production from renewable resources: Metabolic and fermentative strategies.

PubMed

Jiang, Min; Ma, Jiangfeng; Wu, Mingke; Liu, Rongming; Liang, Liya; Xin, Fengxue; Zhang, Wenming; Jia, Honghua; Dong, Weiliang

2017-12-01

Succinic acid is a four-carbon dicarboxylic acid, which has attracted much interest due to its abroad usage as a precursor of many industrially important chemicals in the food, chemicals, and pharmaceutical industries. Facing the shortage of crude oil supply and demand of sustainable development, biological production of succinic acid from renewable resources has become a topic of worldwide interest. In recent decades, robust producing strain selection, metabolic engineering of model strains, and process optimization for succinic acid production have been developed. This review provides an overview of succinic acid producers and cultivation technology, highlight some of the successful metabolic engineering approaches. Copyright © 2017 Elsevier Ltd. All rights reserved.

Secondary metabolism in simulated microgravity: beta-lactam production by Streptomyces clavuligerus

NASA Technical Reports Server (NTRS)

Fang, A.; Pierson, D. L.; Mishra, S. K.; Koenig, D. W.; Demain, A. L.

1997-01-01

Rotating bioreactors designed at NASA's Johnson Space Center were used to simulate a microgravity environment in which to study secondary metabolism. The system examined was beta-lactam antibiotic production by Streptomyces clavuligerus. Both growth and beta-lactam production occurred in simulated microgravity. Stimulatory effects of phosphate and L-lysine, previously detected in normal gravity, also occurred in simulated microgravity. The degree of beta-lactam antibiotic production was markedly inhibited by simulated microgravity.

Biological hydrogen production by dark fermentation: challenges and prospects towards scaled-up production.

PubMed

RenNanqi; GuoWanqian; LiuBingfeng; CaoGuangli; DingJie

2011-06-01

Among different technologies of hydrogen production, bio-hydrogen production exhibits perhaps the greatest potential to replace fossil fuels. Based on recent research on dark fermentative hydrogen production, this article reviews the following aspects towards scaled-up application of this technology: bioreactor development and parameter optimization, process modeling and simulation, exploitation of cheaper raw materials and combining dark-fermentation with photo-fermentation. Bioreactors are necessary for dark-fermentation hydrogen production, so the design of reactor type and optimization of parameters are essential. Process modeling and simulation can help engineers design and optimize large-scale systems and operations. Use of cheaper raw materials will surely accelerate the pace of scaled-up production of biological hydrogen. And finally, combining dark-fermentation with photo-fermentation holds considerable promise, and has successfully achieved maximum overall hydrogen yield from a single substrate. Future development of bio-hydrogen production will also be discussed. Copyright © 2011 Elsevier Ltd. All rights reserved.

In vitro metabolism study of a black market product containing SARM LGD-4033.

PubMed

Geldof, Lore; Pozo, Oscar J; Lootens, Leen; Morthier, Wouter; Van Eenoo, Peter; Deventer, Koen

2017-02-01

Anabolic agents are often used by athletes to enhance their performance. However, use of steroids leads to considerable side effects. Non-steroidal selective androgen receptor modulators (SARMs) are a novel class of substances that have not been approved so far but seem to have a more favourable anabolic/androgenic ratio than steroids and produce fewer side effects. Therefore the use of SARMs has been prohibited since 2008 by the World Anti-Doping Agency (WADA). Several of these SARMs have been detected on the black market. Metabolism studies are essential to identify the best urinary markers to ensure effective control of emerging substances by doping control laboratories. As black market products often contain non-pharmaceutical-grade substances, alternatives for human excretion studies are needed to elucidate the metabolism. A black market product labelled to contain the SARM LGD-4033 was purchased over the Internet. Purity verification of the black market product led to the detection of LGD-4033, without other contaminants. Human liver microsomes and S9 liver fractions were used to perform phase I and phase II (glucuronidation) metabolism studies. The samples of the in vitro metabolism studies were analyzed by gas chromatography-(tandem) mass spectrometry (GC-MS(/MS)), liquid chromatography-high resolution-tandem mass spectrometry (LC-(HR)MS/MS). LC-HRMS product ion scans allowed to identify typical fragment ions for the parent compound and to further determine metabolite structures. In total five metabolites were detected, all modified in the pyrrolidine ring of LGD-4033. The metabolic modifications ranged from hydroxylation combined with keto-formation (M1) or cleavage of the pyrrolidine ring (M2), hydroxylation and methylation (M3/M4) and dihydroxylation (M5). The parent compound and M2 were also detected as glucuronide-conjugates. Copyright © 2016 John Wiley & Sons, Ltd. Copyright © 2016 John Wiley & Sons, Ltd.

Metabolic engineering of microorganisms for the production of L-arginine and its derivatives.

PubMed

Shin, Jae Ho; Lee, Sang Yup

2014-12-03

L-arginine (ARG) is an important amino acid for both medicinal and industrial applications. For almost six decades, the research has been going on for its improved industrial level production using different microorganisms. While the initial approaches involved random mutagenesis for increased tolerance to ARG and consequently higher ARG titer, it is laborious and often leads to unwanted phenotypes, such as retarded growth. Discovery of L-glutamate (GLU) overproducing strains and using them as base strains for ARG production led to improved ARG production titer. Continued effort to unveil molecular mechanisms led to the accumulation of detailed knowledge on amino acid metabolism, which has contributed to better understanding of ARG biosynthesis and its regulation. Moreover, systems metabolic engineering now enables scientists and engineers to efficiently construct genetically defined microorganisms for ARG overproduction in a more rational and system-wide manner. Despite such effort, ARG biosynthesis is still not fully understood and many of the genes in the pathway are mislabeled. Here, we review the major metabolic pathways and its regulation involved in ARG biosynthesis in different prokaryotes including recent discoveries. Also, various strategies for metabolic engineering of bacteria for the overproduction of ARG are described. Furthermore, metabolic engineering approaches for producing ARG derivatives such as L-ornithine (ORN), putrescine and cyanophycin are described. ORN is used in medical applications, while putrescine can be used as a bio-based precursor for the synthesis of nylon-4,6 and nylon-4,10. Cyanophycin is also an important compound for the production of polyaspartate, another important bio-based polymer. Strategies outlined here will serve as a general guideline for rationally designing of cell-factories for overproduction of ARG and related compounds that are industrially valuable.

Continental-scale decrease in net primary productivity in streams due to climate warming

NASA Astrophysics Data System (ADS)

Song, Chao; Dodds, Walter K.; Rüegg, Janine; Argerich, Alba; Baker, Christina L.; Bowden, William B.; Douglas, Michael M.; Farrell, Kaitlin J.; Flinn, Michael B.; Garcia, Erica A.; Helton, Ashley M.; Harms, Tamara K.; Jia, Shufang; Jones, Jeremy B.; Koenig, Lauren E.; Kominoski, John S.; McDowell, William H.; McMaster, Damien; Parker, Samuel P.; Rosemond, Amy D.; Ruffing, Claire M.; Sheehan, Ken R.; Trentman, Matt T.; Whiles, Matt R.; Wollheim, Wilfred M.; Ballantyne, Ford

2018-06-01

Streams play a key role in the global carbon cycle. The balance between carbon intake through photosynthesis and carbon release via respiration influences carbon emissions from streams and depends on temperature. However, the lack of a comprehensive analysis of the temperature sensitivity of the metabolic balance in inland waters across latitudes and local climate conditions hinders an accurate projection of carbon emissions in a warmer future. Here, we use a model of diel dissolved oxygen dynamics, combined with high-frequency measurements of dissolved oxygen, light and temperature, to estimate the temperature sensitivities of gross primary production and ecosystem respiration in streams across six biomes, from the tropics to the arctic tundra. We find that the change in metabolic balance, that is, the ratio of gross primary production to ecosystem respiration, is a function of stream temperature and current metabolic balance. Applying this relationship to the global compilation of stream metabolism data, we find that a 1 °C increase in stream temperature leads to a convergence of metabolic balance and to a 23.6% overall decline in net ecosystem productivity across the streams studied. We suggest that if the relationship holds for similarly sized streams around the globe, the warming-induced shifts in metabolic balance will result in an increase of 0.0194 Pg carbon emitted from such streams every year.

Linking phytoplankton community metabolism to the individual size distribution.

PubMed

Padfield, Daniel; Buckling, Angus; Warfield, Ruth; Lowe, Chris; Yvon-Durocher, Gabriel

2018-05-25

Quantifying variation in ecosystem metabolism is critical to predicting the impacts of environmental change on the carbon cycle. We used a metabolic scaling framework to investigate how body size and temperature influence phytoplankton community metabolism. We tested this framework using phytoplankton sampled from an outdoor mesocosm experiment, where communities had been either experimentally warmed (+ 4 °C) for 10 years or left at ambient temperature. Warmed and ambient phytoplankton communities differed substantially in their taxonomic composition and size structure. Despite this, the response of primary production and community respiration to long- and short-term warming could be estimated using a model that accounted for the size- and temperature dependence of individual metabolism, and the community abundance-body size distribution. This work demonstrates that the key metabolic fluxes that determine the carbon balance of planktonic ecosystems can be approximated using metabolic scaling theory, with knowledge of the individual size distribution and environmental temperature. © 2018 The Authors. Ecology Letters published by CNRS and John Wiley & Sons Ltd.

Viral Activation of Cellular Metabolism

PubMed Central

Sanchez, Erica L.; Lagunoff, Michael

2015-01-01

To ensure optimal environments for their replication and spread, viruses have evolved to alter many host cell pathways. In the last decade, metabolomic studies have shown that eukaryotic viruses induce large-scale alterations in host cellular metabolism. Most viruses examined to date induce aerobic glycolysis also known as the Warburg effect. Many viruses tested also induce fatty acid synthesis as well as glutaminolysis. These modifications of carbon source utilization by infected cells can increase available energy for virus replication and virion production, provide specific cellular substrates for virus particles and create viral replication niches while increasing infected cell survival. Each virus species also likely requires unique metabolic changes for successful spread and recent research has identified additional virus-specific metabolic changes induced by many virus species. A better understanding of the metabolic alterations required for each virus may lead to novel therapeutic approaches through targeted inhibition of specific cellular metabolic pathways. PMID:25812764

Tree Morphologic Plasticity Explains Deviation from Metabolic Scaling Theory in Semi-Arid Conifer Forests, Southwestern USA

PubMed Central

O’Connor, Christopher D.; Lynch, Ann M.

2016-01-01

A significant concern about Metabolic Scaling Theory (MST) in real forests relates to consistent differences between the values of power law scaling exponents of tree primary size measures used to estimate mass and those predicted by MST. Here we consider why observed scaling exponents for diameter and height relationships deviate from MST predictions across three semi-arid conifer forests in relation to: (1) tree condition and physical form, (2) the level of inter-tree competition (e.g. open vs closed stand structure), (3) increasing tree age, and (4) differences in site productivity. Scaling exponent values derived from non-linear least-squares regression for trees in excellent condition (n = 381) were above the MST prediction at the 95% confidence level, while the exponent for trees in good condition were no different than MST (n = 926). Trees that were in fair or poor condition, characterized as diseased, leaning, or sparsely crowned had exponent values below MST predictions (n = 2,058), as did recently dead standing trees (n = 375). Exponent value of the mean-tree model that disregarded tree condition (n = 3,740) was consistent with other studies that reject MST scaling. Ostensibly, as stand density and competition increase trees exhibited greater morphological plasticity whereby the majority had characteristically fair or poor growth forms. Fitting by least-squares regression biases the mean-tree model scaling exponent toward values that are below MST idealized predictions. For 368 trees from Arizona with known establishment dates, increasing age had no significant impact on expected scaling. We further suggest height to diameter ratios below MST relate to vertical truncation caused by limitation in plant water availability. Even with environmentally imposed height limitation, proportionality between height and diameter scaling exponents were consistent with the predictions of MST. PMID:27391084

Tree Morphologic Plasticity Explains Deviation from Metabolic Scaling Theory in Semi-Arid Conifer Forests, Southwestern USA.

PubMed

Swetnam, Tyson L; O'Connor, Christopher D; Lynch, Ann M

2016-01-01

A significant concern about Metabolic Scaling Theory (MST) in real forests relates to consistent differences between the values of power law scaling exponents of tree primary size measures used to estimate mass and those predicted by MST. Here we consider why observed scaling exponents for diameter and height relationships deviate from MST predictions across three semi-arid conifer forests in relation to: (1) tree condition and physical form, (2) the level of inter-tree competition (e.g. open vs closed stand structure), (3) increasing tree age, and (4) differences in site productivity. Scaling exponent values derived from non-linear least-squares regression for trees in excellent condition (n = 381) were above the MST prediction at the 95% confidence level, while the exponent for trees in good condition were no different than MST (n = 926). Trees that were in fair or poor condition, characterized as diseased, leaning, or sparsely crowned had exponent values below MST predictions (n = 2,058), as did recently dead standing trees (n = 375). Exponent value of the mean-tree model that disregarded tree condition (n = 3,740) was consistent with other studies that reject MST scaling. Ostensibly, as stand density and competition increase trees exhibited greater morphological plasticity whereby the majority had characteristically fair or poor growth forms. Fitting by least-squares regression biases the mean-tree model scaling exponent toward values that are below MST idealized predictions. For 368 trees from Arizona with known establishment dates, increasing age had no significant impact on expected scaling. We further suggest height to diameter ratios below MST relate to vertical truncation caused by limitation in plant water availability. Even with environmentally imposed height limitation, proportionality between height and diameter scaling exponents were consistent with the predictions of MST.

Solving gap metabolites and blocked reactions in genome-scale models: application to the metabolic network of Blattabacterium cuenoti.

PubMed

Ponce-de-León, Miguel; Montero, Francisco; Peretó, Juli

2013-10-31

Metabolic reconstruction is the computational-based process that aims to elucidate the network of metabolites interconnected through reactions catalyzed by activities assigned to one or more genes. Reconstructed models may contain inconsistencies that appear as gap metabolites and blocked reactions. Although automatic methods for solving this problem have been previously developed, there are many situations where manual curation is still needed. We introduce a general definition of gap metabolite that allows its detection in a straightforward manner. Moreover, a method for the detection of Unconnected Modules, defined as isolated sets of blocked reactions connected through gap metabolites, is proposed. The method has been successfully applied to the curation of iCG238, the genome-scale metabolic model for the bacterium Blattabacterium cuenoti, obligate endosymbiont of cockroaches. We found the proposed approach to be a valuable tool for the curation of genome-scale metabolic models. The outcome of its application to the genome-scale model B. cuenoti iCG238 is a more accurate model version named as B. cuenoti iMP240.

Metabolic engineering of Clostridium acetobutylicum for the enhanced production of isopropanol-butanol-ethanol fuel mixture.

PubMed

Jang, Yu-Sin; Malaviya, Alok; Lee, Joungmin; Im, Jung Ae; Lee, Sang Yup; Lee, Julia; Eom, Moon-Ho; Cho, Jung-Hee; Seung, Do Young

2013-01-01

Butanol is considered as a superior biofuel, which is conventionally produced by clostridial acetone-butanol-ethanol (ABE) fermentation. Among ABE, only butanol and ethanol can be used as fuel alternatives. Coproduction of acetone thus causes lower yield of fuel alcohols. Thus, this study aimed at developing an improved Clostridium acetobutylicum strain possessing enhanced fuel alcohol production capability. For this, we previously developed a hyper ABE producing BKM19 strain was further engineered to convert acetone into isopropanol. The BKM19 strain was transformed with the plasmid pIPA100 containing the sadh (primary/secondary alcohol dehydrogenase) and hydG (putative electron transfer protein) genes from the Clostridium beijerinckii NRRL B593 cloned under the control of the thiolase promoter. The resulting BKM19 (pIPA100) strain produced 27.9 g/l isopropanol-butanol-ethanol (IBE) as a fuel alcohols with negligible amount of acetone (0.4 g/l) from 97.8 g/l glucose in lab-scale (2 l) batch fermentation. Thus, this metabolically engineered strain was able to produce 99% of total solvent produced as fuel alcohols. The scalability and stability of BKM19 (pIPA100) were evaluated at 200 l pilot-scale fermentation, which showed that the fuel alcohol yield could be improved to 0.37 g/g as compared to 0.29 g/g obtained at lab-scale fermentation, while attaining a similar titer. To the best of our knowledge, this is the highest titer of IBE achieved and the first report on the large scale fermentation of C. acetobutylicum for IBE production. © 2013 American Institute of Chemical Engineers.

Metabolic pathway engineering for fatty acid ethyl ester production in Saccharomyces cerevisiae using stable chromosomal integration.

PubMed

de Jong, Bouke Wim; Shi, Shuobo; Valle-Rodríguez, Juan Octavio; Siewers, Verena; Nielsen, Jens

2015-03-01

Fatty acid ethyl esters are fatty acid derived molecules similar to first generation biodiesel (fatty acid methyl esters; FAMEs) which can be produced in a microbial cell factory. Saccharomyces cerevisiae is a suitable candidate for microbial large scale and long term cultivations, which is the typical industrial production setting for biofuels. It is crucial to conserve the metabolic design of the cell factory during industrial cultivation conditions that require extensive propagation. Genetic modifications therefore have to be introduced in a stable manner. Here, several metabolic engineering strategies for improved production of fatty acid ethyl esters in S. cerevisiae were combined and the genes were stably expressed from the organisms' chromosomes. A wax ester synthase (ws2) was expressed in different yeast strains with an engineered acetyl-CoA and fatty acid metabolism. Thus, we compared expression of ws2 with and without overexpression of alcohol dehydrogenase (ADH2), acetaldehyde dehydrogenase (ALD6) and acetyl-CoA synthetase (acs SE (L641P) ) and further evaluated additional overexpression of a mutant version of acetyl-CoA decarboxylase (ACC1 (S1157A,S659A) ) and the acyl-CoA binding protein (ACB1). The combined engineering efforts of the implementation of ws2, ADH2, ALD6 and acs SE (L641P) , ACC1 (S1157A,S659A) and ACB1 in a S. cerevisiae strain lacking storage lipid formation (are1Δ, are2Δ, dga1Δ and lro1Δ) and β-oxidation (pox1Δ) resulted in a 4.1-fold improvement compared with sole expression of ws2 in S. cerevisiae.

Exometabolome analysis reveals hypoxia at the up-scaling of a Saccharomyces cerevisiae high-cell density fed-batch biopharmaceutical process

PubMed Central

2014-01-01

Background Scale-up to industrial production level of a fermentation process occurs after optimization at small scale, a critical transition for successful technology transfer and commercialization of a product of interest. At the large scale a number of important bioprocess engineering problems arise that should be taken into account to match the values obtained at the small scale and achieve the highest productivity and quality possible. However, the changes of the host strain’s physiological and metabolic behavior in response to the scale transition are still not clear. Results Heterogeneity in substrate and oxygen distribution is an inherent factor at industrial scale (10,000 L) which affects the success of process up-scaling. To counteract these detrimental effects, changes in dissolved oxygen and pressure set points and addition of diluents were applied to 10,000 L scale to enable a successful process scale-up. A comprehensive semi-quantitative and time-dependent analysis of the exometabolome was performed to understand the impact of the scale-up on the metabolic/physiological behavior of the host microorganism. Intermediates from central carbon catabolism and mevalonate/ergosterol synthesis pathways were found to accumulate in both the 10 L and 10,000 L scale cultures in a time-dependent manner. Moreover, excreted metabolites analysis revealed that hypoxic conditions prevailed at the 10,000 L scale. The specific product yield increased at the 10,000 L scale, in spite of metabolic stress and catabolic-anabolic uncoupling unveiled by the decrease in biomass yield on consumed oxygen. Conclusions An optimized S. cerevisiae fermentation process was successfully scaled-up to an industrial scale bioreactor. The oxygen uptake rate (OUR) and overall growth profiles were matched between scales. The major remaining differences between scales were wet cell weight and culture apparent viscosity. The metabolic and physiological behavior of the host microorganism

A multi-tissue type genome-scale metabolic network for analysis of whole-body systems physiology

PubMed Central

2011-01-01

Background Genome-scale metabolic reconstructions provide a biologically meaningful mechanistic basis for the genotype-phenotype relationship. The global human metabolic network, termed Recon 1, has recently been reconstructed allowing the systems analysis of human metabolic physiology and pathology. Utilizing high-throughput data, Recon 1 has recently been tailored to different cells and tissues, including the liver, kidney, brain, and alveolar macrophage. These models have shown utility in the study of systems medicine. However, no integrated analysis between human tissues has been done. Results To describe tissue-specific functions, Recon 1 was tailored to describe metabolism in three human cells: adipocytes, hepatocytes, and myocytes. These cell-specific networks were manually curated and validated based on known cellular metabolic functions. To study intercellular interactions, a novel multi-tissue type modeling approach was developed to integrate the metabolic functions for the three cell types, and subsequently used to simulate known integrated metabolic cycles. In addition, the multi-tissue model was used to study diabetes: a pathology with systemic properties. High-throughput data was integrated with the network to determine differential metabolic activity between obese and type II obese gastric bypass patients in a whole-body context. Conclusion The multi-tissue type modeling approach presented provides a platform to study integrated metabolic states. As more cell and tissue-specific models are released, it is critical to develop a framework in which to study their interdependencies. PMID:22041191

Metabolic Engineering for Enhanced Medium Chain Omega Hydroxy Fatty Acid Production in Escherichia coli

PubMed Central

Xiao, Kang; Yue, Xiu-Hong; Chen, Wen-Chao; Zhou, Xue-Rong; Wang, Lian; Xu, Lin; Huang, Feng-Hong; Wan, Xia

2018-01-01

Medium chain hydroxy fatty acids (HFAs) at ω-1, 2, or 3 positions (ω-1/2/3) are rare in nature but are attractive due to their potential applications in industry. They can be metabolically engineered in Escherichia coli, however, the current yield is low. In this study, metabolic engineering with P450BM3 monooxygenase was applied to regulate both the chain length and sub-terminal position of HFA products in E. coli, leading to increased yield. Five acyl-acyl carrier protein thioesterases from plants and bacteria were first evaluated for regulating the chain length of fatty acids. Co-expression of the selected thioesterase gene CcFatB1 with a fatty acid metabolism regulator fadR and monooxygenase P450BM3 boosted the production of HFAs especially ω-3-OH-C14:1, in both the wild type and fadD deficient strain. Supplementing renewable glycerol to reduce the usage of glucose as a carbon source further increased the HFAs production to 144 mg/L, representing the highest titer of such HFAs obtained in E. coli under the comparable conditions. This study illustrated an improved metabolic strategy for medium chain ω-1/2/3 HFAs production in E. coli. In addition, the produced HFAs were mostly secreted into culture media, which eased its recovery. PMID:29467747

Production of C2-C4 diols from renewable bioresources: new metabolic pathways and metabolic engineering strategies.

PubMed

Zhang, Ye; Liu, Dehua; Chen, Zhen

2017-01-01

C2-C4 diols classically derived from fossil resource are very important bulk chemicals which have been used in a wide range of areas, including solvents, fuels, polymers, cosmetics, and pharmaceuticals. Production of C2-C4 diols from renewable resources has received significant interest in consideration of the reducing fossil resource and the increasing environmental issues. While bioproduction of certain diols like 1,3-propanediol has been commercialized in recent years, biosynthesis of many other important C2-C4 diol isomers is highly challenging due to the lack of natural synthesis pathways. Recent advances in synthetic biology have enabled the de novo design of completely new pathways to non-natural molecules from renewable feedstocks. In this study, we review recent advances in bioproduction of C2-C4 diols, focusing on new metabolic pathways and metabolic engineering strategies being developed. We also discuss the challenges and future trends toward the development of economically competitive processes for bio-based diol production.

Metabolic engineering of microorganisms for biofuels production: from bugs to synthetic biology to fuels.

PubMed

Lee, Sung Kuk; Chou, Howard; Ham, Timothy S; Lee, Taek Soon; Keasling, Jay D

2008-12-01

The ability to generate microorganisms that can produce biofuels similar to petroleum-based transportation fuels would allow the use of existing engines and infrastructure and would save an enormous amount of capital required for replacing the current infrastructure to accommodate biofuels that have properties significantly different from petroleum-based fuels. Several groups have demonstrated the feasibility of manipulating microbes to produce molecules similar to petroleum-derived products, albeit at relatively low productivity (e.g. maximum butanol production is around 20 g/L). For cost-effective production of biofuels, the fuel-producing hosts and pathways must be engineered and optimized. Advances in metabolic engineering and synthetic biology will provide new tools for metabolic engineers to better understand how to rewire the cell in order to create the desired phenotypes for the production of economically viable biofuels.

Genome-scale estimate of the metabolic turnover of E. Coli from the energy balance analysis

NASA Astrophysics Data System (ADS)

De Martino, D.

2016-02-01

In this article the notion of metabolic turnover is revisited in the light of recent results of out-of-equilibrium thermodynamics. By means of Monte Carlo methods we perform an exact sampling of the enzymatic fluxes in a genome scale metabolic network of E. Coli in stationary growth conditions from which we infer the metabolites turnover times. However the latter are inferred from net fluxes, and we argue that this approximation is not valid for enzymes working nearby thermodynamic equilibrium. We recalculate turnover times from total fluxes by performing an energy balance analysis of the network and recurring to the fluctuation theorem. We find in many cases values one of order of magnitude lower, implying a faster picture of intermediate metabolism.

Systematic assignment of thermodynamic constraints in metabolic network models

PubMed Central

Kümmel, Anne; Panke, Sven; Heinemann, Matthias

2006-01-01

Background The availability of genome sequences for many organisms enabled the reconstruction of several genome-scale metabolic network models. Currently, significant efforts are put into the automated reconstruction of such models. For this, several computational tools have been developed that particularly assist in identifying and compiling the organism-specific lists of metabolic reactions. In contrast, the last step of the model reconstruction process, which is the definition of the thermodynamic constraints in terms of reaction directionalities, still needs to be done manually. No computational method exists that allows for an automated and systematic assignment of reaction directions in genome-scale models. Results We present an algorithm that – based on thermodynamics, network topology and heuristic rules – automatically assigns reaction directions in metabolic models such that the reaction network is thermodynamically feasible with respect to the production of energy equivalents. It first exploits all available experimentally derived Gibbs energies of formation to identify irreversible reactions. As these thermodynamic data are not available for all metabolites, in a next step, further reaction directions are assigned on the basis of network topology considerations and thermodynamics-based heuristic rules. Briefly, the algorithm identifies reaction subsets from the metabolic network that are able to convert low-energy co-substrates into their high-energy counterparts and thus net produce energy. Our algorithm aims at disabling such thermodynamically infeasible cyclic operation of reaction subnetworks by assigning reaction directions based on a set of thermodynamics-derived heuristic rules. We demonstrate our algorithm on a genome-scale metabolic model of E. coli. The introduced systematic direction assignment yielded 130 irreversible reactions (out of 920 total reactions), which corresponds to about 70% of all irreversible reactions that are required to

Susceptibility of Candida albicans biofilms to caspofungin and anidulafungin is not affected by metabolic activity or biomass production.

PubMed

Marcos-Zambrano, Laura Judith; Escribano, Pilar; Bouza, Emilio; Guinea, Jesús

2016-02-01

Micafungin is more active against biofilms with high metabolic activity; however, it is unknown whether this observation applies to caspofungin and anidulafungin and whether it is also dependent on the biomass production. We compare the antifungal activity of anidulafungin, caspofungin, and micafungin against preformed Candida albicans biofilms with different degrees of metabolic activity and biomass production from 301 isolates causing fungemia in patients admitted to Gregorio Marañon Hospital (January 2007 to September 2014). Biofilms were classified as having low, moderate, or high metabolic activity according XTT reduction assay or having low, moderate, or high biomass according to crystal violet assay. Echinocandin MICs for planktonic and sessile cells were measured using the EUCAST E.Def 7.2 procedure and XTT reduction assay, respectively. Micafungin showed the highest activity against biofilms classified according to the metabolic activity and biomass production (P < .001). The activity of caspofungin and anidulafungin was not dependent on the metabolic activity of the biofilm or the biomass production. These observations were confirmed by scanning electron microscopy. None of the echinocandins produced major changes in the structure of biofilms with low metabolic activity and biomass production when compared with the untreated biofilms. However, biofilm with high metabolic activity or high biomass production was considerably more susceptible to micafungin; this effect was not shown by caspofungin or anidulafungin. © The Author 2015. Published by Oxford University Press on behalf of The International Society for Human and Animal Mycology. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Returns to Scale in the Production of Hospital Services

PubMed Central

Berry, Ralph E.

1967-01-01

The primary purpose of this article is to investigate whether or not economies of scale exist in the production of hospital services. In previous studies the results have implied the existence of economies of scale, but the question has not been satisfactorily resolved. The factor most responsible for clouding the issue is the overwhelming prevalence of product differences in the outputs of hospitals. In this study a method which avoids the problem of product differentiation is developed. The analysis strongly supports the conclusion that hospital services are produced subject to economies of scale. PMID:6054380

Slow swimming, fast strikes: effects of feeding behavior on scaling of anaerobic metabolism in epipelagic squid.

PubMed

Trueblood, Lloyd A; Seibel, Brad A

2014-08-01

Many pelagic fishes engage prey at high speeds supported by high metabolic rates and anaerobic metabolic capacity. Epipelagic squids are reported to have among the highest metabolic rates in the oceans as a result of demanding foraging strategies and the use of jet propulsion, which is inherently inefficient. This study examined enzymatic proxies of anaerobic metabolism in two species of pelagic squid, Dosidicus gigas and Doryteuthis pealeii (Lesueur 1821), over a size range of six orders of magnitude. We hypothesized that activity of the anaerobically poised enzymes would be high and increase with size as in ecologically similar fishes. In contrast, we demonstrate that anaerobic metabolic capacity in these organisms scales negatively with body mass. We explored several cephalopod-specific traits, such as the use of tentacles to capture prey, body morphology and reduced relative prey size of adult squids, that may create a diminished reliance on anaerobically fueled burst activity during prey capture in large animals. © 2014. Published by The Company of Biologists Ltd.

Recent advances in microbial production of fuels and chemicals using tools and strategies of systems metabolic engineering.

PubMed

Cho, Changhee; Choi, So Young; Luo, Zi Wei; Lee, Sang Yup

2015-11-15

The advent of various systems metabolic engineering tools and strategies has enabled more sophisticated engineering of microorganisms for the production of industrially useful fuels and chemicals. Advances in systems metabolic engineering have been made in overproducing natural chemicals and producing novel non-natural chemicals. In this paper, we review the tools and strategies of systems metabolic engineering employed for the development of microorganisms for the production of various industrially useful chemicals belonging to fuels, building block chemicals, and specialty chemicals, in particular focusing on those reported in the last three years. It was aimed at providing the current landscape of systems metabolic engineering and suggesting directions to address future challenges towards successfully establishing processes for the bio-based production of fuels and chemicals from renewable resources. Copyright © 2014 Elsevier Inc. All rights reserved.

Factors Associated with Metabolic Syndrome and Related Medical Costs by the Scale of Enterprise in Korea

PubMed Central

2013-01-01

Objectives The purpose of this study was to identify the risk factors of metabolic syndrome (MS) and to analyze the relationship between the risk factors of MS and medical cost of major diseases related to MS in Korean workers, according to the scale of the enterprise. Methods Data was obtained from annual physical examinations, health insurance qualification and premiums, and health insurance benefits of 4,094,217 male and female workers who underwent medical examinations provided by the National Health Insurance Corporation in 2009. Logistic regression analyses were used to the identify risk factors of MS and multiple regression was used to find factors associated with medical expenditures due to major diseases related to MS. Result The study found that low-income workers were more likely to work in small-scale enterprises. The prevalence rate of MS in males and females, respectively, was 17.2% and 9.4% in small-scale enterprises, 15.9% and 8.9% in medium-scale enterprises, and 15.9% and 5.5% in large-scale enterprises. The risks of MS increased with age, lower income status, and smoking in small-scale enterprise workers. The medical costs increased in workers with old age and past smoking history. There was also a gender difference in the pattern of medical expenditures related to MS. Conclusions Health promotion programs to manage metabolic syndrome should be developed to focus on workers who smoke, drink, and do little exercise in small scale enterprises. PMID:24472134

Factors associated with metabolic syndrome and related medical costs by the scale of enterprise in Korea.

PubMed

Kong, Hyung-Sik; Lee, Kang-Sook; Yim, Eun-Shil; Lee, Seon-Young; Cho, Hyun-Young; Lee, Bin Na; Park, Jee Young

2013-10-21

The purpose of this study was to identify the risk factors of metabolic syndrome (MS) and to analyze the relationship between the risk factors of MS and medical cost of major diseases related to MS in Korean workers, according to the scale of the enterprise. Data was obtained from annual physical examinations, health insurance qualification and premiums, and health insurance benefits of 4,094,217 male and female workers who underwent medical examinations provided by the National Health Insurance Corporation in 2009. Logistic regression analyses were used to the identify risk factors of MS and multiple regression was used to find factors associated with medical expenditures due to major diseases related to MS. The study found that low-income workers were more likely to work in small-scale enterprises. The prevalence rate of MS in males and females, respectively, was 17.2% and 9.4% in small-scale enterprises, 15.9% and 8.9% in medium-scale enterprises, and 15.9% and 5.5% in large-scale enterprises. The risks of MS increased with age, lower income status, and smoking in small-scale enterprise workers. The medical costs increased in workers with old age and past smoking history. There was also a gender difference in the pattern of medical expenditures related to MS. Health promotion programs to manage metabolic syndrome should be developed to focus on workers who smoke, drink, and do little exercise in small scale enterprises.

Effect of amino acid supplementation on titer and glycosylation distribution in hybridoma cell cultures-Systems biology-based interpretation using genome-scale metabolic flux balance model and multivariate data analysis.

PubMed

Reimonn, Thomas M; Park, Seo-Young; Agarabi, Cyrus D; Brorson, Kurt A; Yoon, Seongkyu

2016-09-01

Genome-scale flux balance analysis (FBA) is a powerful systems biology tool to characterize intracellular reaction fluxes during cell cultures. FBA estimates intracellular reaction rates by optimizing an objective function, subject to the constraints of a metabolic model and media uptake/excretion rates. A dynamic extension to FBA, dynamic flux balance analysis (DFBA), can calculate intracellular reaction fluxes as they change during cell cultures. In a previous study by Read et al. (2013), a series of informed amino acid supplementation experiments were performed on twelve parallel murine hybridoma cell cultures, and this data was leveraged for further analysis (Read et al., Biotechnol Prog. 2013;29:745-753). In order to understand the effects of media changes on the model murine hybridoma cell line, a systems biology approach is applied in the current study. Dynamic flux balance analysis was performed using a genome-scale mouse metabolic model, and multivariate data analysis was used for interpretation. The calculated reaction fluxes were examined using partial least squares and partial least squares discriminant analysis. The results indicate media supplementation increases product yield because it raises nutrient levels extending the growth phase, and the increased cell density allows for greater culture performance. At the same time, the directed supplementation does not change the overall metabolism of the cells. This supports the conclusion that product quality, as measured by glycoform assays, remains unchanged because the metabolism remains in a similar state. Additionally, the DFBA shows that metabolic state varies more at the beginning of the culture but less by the middle of the growth phase, possibly due to stress on the cells during inoculation. © 2016 American Institute of Chemical Engineers Biotechnol. Prog., 32:1163-1173, 2016. © 2016 American Institute of Chemical Engineers.

Ruminant Nutrition Symposium: ruminant production and metabolic responses to heat stress.

PubMed

Baumgard, L H; Rhoads, R P

2012-06-01

Heat stress compromises efficient animal production by marginalizing nutrition, management, and genetic selection efforts to maximize performance endpoints. Modifying farm infrastructure has yielded modest success in mitigating heat stress-related losses, yet poor production during the summer remains arguably the costliest issue facing livestock producers. Reduced output (e.g., milk yield and muscle growth) during heat stress was traditionally thought to result from decreased nutrient intake (i.e., a classic biological response shared by all animals during environmental-induced hyperthermia). Our recent observations have begun to challenge this belief and indicate heat-stressed animals employ novel homeorhetic strategies to direct metabolic and fuel selection priorities independently of nutrient intake or energy balance. Alterations in systemic physiology support a shift in carbohydrate metabolism, evident by increased basal and stimulated circulating insulin concentrations. Perhaps most intriguing given the energetic shortfall of the heat-stressed animal is the apparent lack of basal adipose tissue mobilization coupled with a reduced responsiveness to lipolytic stimuli. Thus, the heat stress response markedly alters postabsorptive carbohydrate, lipid, and protein metabolism independently of reduced feed intake through coordinated changes in fuel supply and utilization by multiple tissues. Interestingly, the systemic, cellular, and molecular changes appear conserved amongst different species and physiological states. Ultimately, these changes result in the reprioritization of fuel selection during heat stress, which appears to be primarily responsible for reduced ruminant animal productivity during the warm summer months.

Whole-body CO2 production as an index of the metabolic response to sepsis

USDA-ARS?s Scientific Manuscript database

Whole-body carbon dioxide (CO2) production (RaCO2) is an index of substrate oxidation and energy expenditure; therefore, it may provide information about the metabolic response to sepsis. Using stable isotope techniques, we determined RaCO2 and its relationship to protein and glucose metabolism in m...

Comparative genome-scale modelling of Staphylococcus aureus strains identifies strain-specific metabolic capabilities linked to pathogenicity

PubMed Central

Bosi, Emanuele; Monk, Jonathan M.; Aziz, Ramy K.; Fondi, Marco; Nizet, Victor; Palsson, Bernhard Ø.

2016-01-01

Staphylococcus aureus is a preeminent bacterial pathogen capable of colonizing diverse ecological niches within its human host. We describe here the pangenome of S. aureus based on analysis of genome sequences from 64 strains of S. aureus spanning a range of ecological niches, host types, and antibiotic resistance profiles. Based on this set, S. aureus is expected to have an open pangenome composed of 7,411 genes and a core genome composed of 1,441 genes. Metabolism was highly conserved in this core genome; however, differences were identified in amino acid and nucleotide biosynthesis pathways between the strains. Genome-scale models (GEMs) of metabolism were constructed for the 64 strains of S. aureus. These GEMs enabled a systems approach to characterizing the core metabolic and panmetabolic capabilities of the S. aureus species. All models were predicted to be auxotrophic for the vitamins niacin (vitamin B3) and thiamin (vitamin B1), whereas strain-specific auxotrophies were predicted for riboflavin (vitamin B2), guanosine, leucine, methionine, and cysteine, among others. GEMs were used to systematically analyze growth capabilities in more than 300 different growth-supporting environments. The results identified metabolic capabilities linked to pathogenic traits and virulence acquisitions. Such traits can be used to differentiate strains responsible for mild vs. severe infections and preference for hosts (e.g., animals vs. humans). Genome-scale analysis of multiple strains of a species can thus be used to identify metabolic determinants of virulence and increase our understanding of why certain strains of this deadly pathogen have spread rapidly throughout the world. PMID:27286824

A Multi-scale Computational Platform to Mechanistically Assess the Effect of Genetic Variation on Drug Responses in Human Erythrocyte Metabolism.

PubMed

Mih, Nathan; Brunk, Elizabeth; Bordbar, Aarash; Palsson, Bernhard O

2016-07-01

Progress in systems medicine brings promise to addressing patient heterogeneity and individualized therapies. Recently, genome-scale models of metabolism have been shown to provide insight into the mechanistic link between drug therapies and systems-level off-target effects while being expanded to explicitly include the three-dimensional structure of proteins. The integration of these molecular-level details, such as the physical, structural, and dynamical properties of proteins, notably expands the computational description of biochemical network-level properties and the possibility of understanding and predicting whole cell phenotypes. In this study, we present a multi-scale modeling framework that describes biological processes which range in scale from atomistic details to an entire metabolic network. Using this approach, we can understand how genetic variation, which impacts the structure and reactivity of a protein, influences both native and drug-induced metabolic states. As a proof-of-concept, we study three enzymes (catechol-O-methyltransferase, glucose-6-phosphate dehydrogenase, and glyceraldehyde-3-phosphate dehydrogenase) and their respective genetic variants which have clinically relevant associations. Using all-atom molecular dynamic simulations enables the sampling of long timescale conformational dynamics of the proteins (and their mutant variants) in complex with their respective native metabolites or drug molecules. We find that changes in a protein's structure due to a mutation influences protein binding affinity to metabolites and/or drug molecules, and inflicts large-scale changes in metabolism.

Metabolic engineering of microorganisms for biofuels production: from bugs to synthetic biology to fuels

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kuk Lee, Sung; Chou, Howard; Ham, Timothy S.

2009-12-02

The ability to generate microorganisms that can produce biofuels similar to petroleum-based transportation fuels would allow the use of existing engines and infrastructure and would save an enormous amount of capital required for replacing the current infrastructure to accommodate biofuels that have properties significantly different from petroleum-based fuels. Several groups have demonstrated the feasibility of manipulating microbes to produce molecules similar to petroleum-derived products, albeit at relatively low productivity (e.g. maximum butanol production is around 20 g/L). For cost-effective production of biofuels, the fuel-producing hosts and pathways must be engineered and optimized. Advances in metabolic engineering and synthetic biology willmore » provide new tools for metabolic engineers to better understand how to rewire the cell in order to create the desired phenotypes for the production of economically viable biofuels.« less

Metabolic engineering of Escherichia coli for production of valerenadiene.

PubMed

Nybo, S Eric; Saunders, Jacqueline; McCormick, Sean P

2017-11-20

Valeriana officinalis is a medicinal herb which produces a suite of compounds in its root tissue useful for treatment of anxiety and insomnia. The sesquiterpene components of the root extract, valerenic acid and valerena-1,10-diene, are thought to contribute to most of the observed anxiolytic of Valerian root preparations. However, valerenic acid and its biosynthetic intermediates are only produced in low quantities in the roots of V. officinalis. Thus, in this report, Escherichia coli was metabolically engineered to produce substantial quantities of valerena-1,10-diene in shake flask fermentations with decane overlay. Expression of the wildtype valerenadiene synthase gene (pZE-wvds) resulted in production of 12μg/mL in LB cultures using endogenous FPP metabolism. Expression of a codon-optimized version of the valerenadiene synthase gene (pZE-cvds) resulted in 3-fold higher titers of valerenadiene (32μg/mL). Co-expression of pZE-cvds with an engineered methyl erythritol phosphate (MEP) pathway improved valerenadiene titers 65-fold to 2.09mg/L valerenadiene. Optimization of the fermentation medium to include glycerol supplementation enhanced yields by another 5.5-fold (11.0mg/L valerenadiene). The highest production of valerenadiene resulted from engineering the codon-optimized valerenadiene synthase gene under strong P trc and P T7 promoters and via co-expression of an exogenous mevalonate (MVA) pathway. These efforts resulted in an E. coli production strain that produced 62.0mg/L valerenadiene (19.4mg/L/OD 600 specific productivity). This E. coli production platform will serve as the foundation for the synthesis of novel valerenic acid analogues potentially useful for the treatment of anxiety disorders. Copyright © 2017 Elsevier B.V. All rights reserved.

Metabolic reconstruction and flux analysis of industrial Pichia yeasts.

PubMed

Chung, Bevan Kai-Sheng; Lakshmanan, Meiyappan; Klement, Maximilian; Ching, Chi Bun; Lee, Dong-Yup

2013-03-01

Pichia yeasts have been recognized as important microbial cell factories in the biotechnological industry. Notably, the Pichia pastoris and Pichia stipitis species have attracted much research interest due to their unique cellular physiology and metabolic capability: P. pastoris has the ability to utilize methanol for cell growth and recombinant protein production, while P. stipitis is capable of assimilating xylose to produce ethanol under oxygen-limited conditions. To harness these characteristics for biotechnological applications, it is highly required to characterize their metabolic behavior. Recently, following the genome sequencing of these two Pichia species, genome-scale metabolic networks have been reconstructed to model the yeasts' metabolism from a systems perspective. To date, there are three genome-scale models available for each of P. pastoris and P. stipitis. In this mini-review, we provide an overview of the models, discuss certain limitations of previous studies, and propose potential future works that can be conducted to better understand and engineer Pichia yeasts for industrial applications.

Statistical total correlation spectroscopy scaling for enhancement of metabolic information recovery in biological NMR spectra.

PubMed

Maher, Anthony D; Fonville, Judith M; Coen, Muireann; Lindon, John C; Rae, Caroline D; Nicholson, Jeremy K

2012-01-17

The high level of complexity in nuclear magnetic resonance (NMR) metabolic spectroscopic data sets has fueled the development of experimental and mathematical techniques that enhance latent biomarker recovery and improve model interpretability. We previously showed that statistical total correlation spectroscopy (STOCSY) can be used to edit NMR spectra to remove drug metabolite signatures that obscure metabolic variation of diagnostic interest. Here, we extend this "STOCSY editing" concept to a generalized scaling procedure for NMR data that enhances recovery of latent biochemical information and improves biological classification and interpretation. We call this new procedure STOCSY-scaling (STOCSY(S)). STOCSY(S) exploits the fixed proportionality in a set of NMR spectra between resonances from the same molecule to suppress or enhance features correlated with a resonance of interest. We demonstrate this new approach using two exemplar data sets: (a) a streptozotocin rat model (n = 30) of type 1 diabetes and (b) a human epidemiological study utilizing plasma NMR spectra of patients with metabolic syndrome (n = 67). In both cases significant biomarker discovery improvement was observed by using STOCSY(S): the approach successfully suppressed interfering NMR signals from glucose and lactate that otherwise dominate the variation in the streptozotocin study, which then allowed recovery of biomarkers such as glycine, which were otherwise obscured. In the metabolic syndrome study, we used STOCSY(S) to enhance variation from the high-density lipoprotein cholesterol peak, improving the prediction of individuals with metabolic syndrome from controls in orthogonal projections to latent structures discriminant analysis models and facilitating the biological interpretation of the results. Thus, STOCSY(S) is a versatile technique that is applicable in any situation in which variation, either biological or otherwise, dominates a data set at the expense of more interesting or

Unit Price Scaling Trends for Chemical Products

DOE Office of Scientific and Technical Information (OSTI.GOV)

Qi, Wei; Sathre, Roger; William R. Morrow, III

2015-08-01

To facilitate early-stage life-cycle techno-economic modeling of emerging technologies, here we identify scaling relations between unit price and sales quantity for a variety of chemical products of three categories - metal salts, organic compounds, and solvents. We collect price quotations for lab-scale and bulk purchases of chemicals from both U.S. and Chinese suppliers. We apply a log-log linear regression model to estimate the price discount effect. Using the median discount factor of each category, one can infer bulk prices of products for which only lab-scale prices are available. We conduct out-of-sample tests showing that most of the price proxies deviatemore » from their actual reference prices by a factor less than ten. We also apply the bootstrap method to determine if a sample median discount factor should be accepted for price approximation. We find that appropriate discount factors for metal salts and for solvents are both -0.56, while that for organic compounds is -0.67 and is less representative due to greater extent of product heterogeneity within this category.« less

Evaluation of a Genome-Scale In Silico Metabolic Model for Geobacter metallireducens Using Proteomic Data from a Field Biostimulation Experiment

DOE Office of Scientific and Technical Information (OSTI.GOV)

Fang, Yilin; Wilkins, Michael J.; Yabusaki, Steven B.

2012-12-12

Biomass and shotgun global proteomics data that reflected relative protein abundances from samples collected during the 2008 experiment at the U.S. Department of Energy Integrated Field-Scale Subsurface Research Challenge site in Rifle, Colorado, provided an unprecedented opportunity to validate a genome-scale metabolic model of Geobacter metallireducens and assess its performance with respect to prediction of metal reduction, biomass yield, and growth rate under dynamic field conditions. Reconstructed from annotated genomic sequence, biochemical, and physiological data, the constraint-based in silico model of G. metallireducens relates an annotated genome sequence to the physiological functions with 697 reactions controlled by 747 enzyme-coding genes.more » Proteomic analysis showed that 180 of the 637 G. metallireducens proteins detected during the 2008 experiment were associated with specific metabolic reactions in the in silico model. When the field-calibrated Fe(III) terminal electron acceptor process reaction in a reactive transport model for the field experiments was replaced with the genome-scale model, the model predicted that the largest metabolic fluxes through the in silico model reactions generally correspond to the highest abundances of proteins that catalyze those reactions. Central metabolism predicted by the model agrees well with protein abundance profiles inferred from proteomic analysis. Model discrepancies with the proteomic data, such as the relatively low fluxes through amino acid transport and metabolism, revealed pathways or flux constraints in the in silico model that could be updated to more accurately predict metabolic processes that occur in the subsurface environment.« less

River Metabolism and Nutrient Cycling at the Point Scale: Insights from In Situ Sensors in Benthic Chambers

NASA Astrophysics Data System (ADS)

Cohen, M. J.; Reijo, C. J.; Hensley, R. T.

2017-12-01

Riverine processing of nutrients and carbon is a local process, subject to heterogeneity in sediment, biotic, insolation, and flow velocity drivers. Measurements at the reach scale aggregate across riverscapes, limiting their utility for enumerating these drivers, and thus for scaling to river networks. Using a combination of in situ sensors that sample water chemistry at high temporal resolution and open benthic chambers that isolate the biogeochemical impacts of a small footprint of benthic surface area, we explored controls on metabolism and nutrient cycling. We specifically sought to answer two questions. First, what are the controls on primary production, with a particular emphasis on the relative roles of light vs. nutrient limitation? Second, what are the pathways of nutrient retention, and do the reaction kinetics of these different pathways differ? We demonstrate the considerable utility of these benthic chambers, reasoning that they provide experimental units for river processes that are not attainable at the reach or network scale. Specifically, in addition to their ability to sample the heterogeneity of the river bed as well as observe nutrient depletion to create concentrations well below ambient levels, they enable manipulative experiments (e.g., nutrient enrichment, light reduction, grazer adjustments) while retaining key elements of the natural system. Across several of Florida's spring-fed river sites, our results strongly support the primacy of light limitation of primary production, with very little evidence of any incremental effects of nutrient enrichment. Nutrient depletion assays further support the dominance of two N retention mechanisms (denitrification and assimilation), the kinetics of which differ markedly, with denitrification exhibiting nearly first-order reactions, and assimilation following zero-order or Michaelis-Menten kinetics over the range of observed concentrations. This latter result helps explain the absence of strong

Enhancing microbial production of biofuels by expanding microbial metabolic pathways.

PubMed

Yu, Ping; Chen, Xingge; Li, Peng

2017-09-01

Fatty acid, isoprenoid, and alcohol pathways have been successfully engineered to produce biofuels. By introducing three genes, atfA, adhE, and pdc, into Escherichia coli to expand fatty acid pathway, up to 1.28 g/L of fatty acid ethyl esters can be achieved. The isoprenoid pathway can be expanded to produce bisabolene with a high titer of 900 mg/L in Saccharomyces cerevisiae. Short- and long-chain alcohols can also be effectively biosynthesized by extending the carbon chain of ketoacids with an engineered "+1" alcohol pathway. Thus, it can be concluded that expanding microbial metabolic pathways has enormous potential for enhancing microbial production of biofuels for future industrial applications. However, some major challenges for microbial production of biofuels should be overcome to compete with traditional fossil fuels: lowering production costs, reducing the time required to construct genetic elements and to increase their predictability and reliability, and creating reusable parts with useful and predictable behavior. To address these challenges, several aspects should be further considered in future: mining and transformation of genetic elements related to metabolic pathways, assembling biofuel elements and coordinating their functions, enhancing the tolerance of host cells to biofuels, and creating modular subpathways that can be easily interconnected. © 2016 International Union of Biochemistry and Molecular Biology, Inc.

Identification of functional differences in metabolic networks using comparative genomics and constraint-based models.

PubMed

Hamilton, Joshua J; Reed, Jennifer L

2012-01-01

Genome-scale network reconstructions are useful tools for understanding cellular metabolism, and comparisons of such reconstructions can provide insight into metabolic differences between organisms. Recent efforts toward comparing genome-scale models have focused primarily on aligning metabolic networks at the reaction level and then looking at differences and similarities in reaction and gene content. However, these reaction comparison approaches are time-consuming and do not identify the effect network differences have on the functional states of the network. We have developed a bilevel mixed-integer programming approach, CONGA, to identify functional differences between metabolic networks by comparing network reconstructions aligned at the gene level. We first identify orthologous genes across two reconstructions and then use CONGA to identify conditions under which differences in gene content give rise to differences in metabolic capabilities. By seeking genes whose deletion in one or both models disproportionately changes flux through a selected reaction (e.g., growth or by-product secretion) in one model over another, we are able to identify structural metabolic network differences enabling unique metabolic capabilities. Using CONGA, we explore functional differences between two metabolic reconstructions of Escherichia coli and identify a set of reactions responsible for chemical production differences between the two models. We also use this approach to aid in the development of a genome-scale model of Synechococcus sp. PCC 7002. Finally, we propose potential antimicrobial targets in Mycobacterium tuberculosis and Staphylococcus aureus based on differences in their metabolic capabilities. Through these examples, we demonstrate that a gene-centric approach to comparing metabolic networks allows for a rapid comparison of metabolic models at a functional level. Using CONGA, we can identify differences in reaction and gene content which give rise to different

Identification of Functional Differences in Metabolic Networks Using Comparative Genomics and Constraint-Based Models

PubMed Central

Hamilton, Joshua J.; Reed, Jennifer L.

2012-01-01

Genome-scale network reconstructions are useful tools for understanding cellular metabolism, and comparisons of such reconstructions can provide insight into metabolic differences between organisms. Recent efforts toward comparing genome-scale models have focused primarily on aligning metabolic networks at the reaction level and then looking at differences and similarities in reaction and gene content. However, these reaction comparison approaches are time-consuming and do not identify the effect network differences have on the functional states of the network. We have developed a bilevel mixed-integer programming approach, CONGA, to identify functional differences between metabolic networks by comparing network reconstructions aligned at the gene level. We first identify orthologous genes across two reconstructions and then use CONGA to identify conditions under which differences in gene content give rise to differences in metabolic capabilities. By seeking genes whose deletion in one or both models disproportionately changes flux through a selected reaction (e.g., growth or by-product secretion) in one model over another, we are able to identify structural metabolic network differences enabling unique metabolic capabilities. Using CONGA, we explore functional differences between two metabolic reconstructions of Escherichia coli and identify a set of reactions responsible for chemical production differences between the two models. We also use this approach to aid in the development of a genome-scale model of Synechococcus sp. PCC 7002. Finally, we propose potential antimicrobial targets in Mycobacterium tuberculosis and Staphylococcus aureus based on differences in their metabolic capabilities. Through these examples, we demonstrate that a gene-centric approach to comparing metabolic networks allows for a rapid comparison of metabolic models at a functional level. Using CONGA, we can identify differences in reaction and gene content which give rise to different

Size Matters — Physiological Temperature Acclimation and Metabolic Scaling of Respiration for Eucalyptus Trees in a Warmer World

NASA Astrophysics Data System (ADS)

Drake, J. E.; Toelker, M. G.; Reich, P. B.

2016-12-01

Respiration drives the metabolism and growth of trees and represents a large and uncertain component of land surface feedbacks to climate change. A fixed scaling relationship between body mass and respiration has been described as a fundamental law across plants and animals, but this has been controversial. There is now ample evidence that trees adjust their respiration rates in response to temperature variation in their growth environment through physiological acclimation. Is acclimation sufficiently large to alter the scaling relationship between respiration and mass? Here, we make continuous measurements of in-situ­ respiration rates complemented with repeated measurements at a defined set temperature of 15°C for leaves and the entire aboveground component of Eucalyptus parramattensis and E. tereticornis trees growing in the field in warming experiments (ambient vs. +3°C) using 12 whole tree chambers in Australia. We report thousands of repeated measurements as trees grew from 1 to 9-m-tall, allowing a concurrent evaluation of physiological acclimation and metabolic scaling. Trees adjusted the respiration rates of leaves and whole-crowns in relation to the air temperature of the preceding three days, such that: (1) respiration rate per unit mass was reduced by warming when measured at a common temperature, and (2) in-situ whole-crown respiration rates per unit mass were equivalent across ambient and warmed trees (i.e., homeostatic respiration). Acclimation appeared to modify the scaling between respiration and mass, as the slope and intercept of this relationship were affected by recent air temperature. This suggests that metabolic scaling is not fixed, although the overall allometric scaling slope was consistent with the theoretical value of 0.75 (95% CI of 0.5 to 0.78). We suggest that considering acclimation and tree mass together provides new insight into a dynamic scaling of tree respiration, with implications for land surface feedbacks under climate

Systems metabolic engineering of Corynebacterium glutamicum for production of the chemical chaperone ectoine.

PubMed

Becker, Judith; Schäfer, Rudolf; Kohlstedt, Michael; Harder, Björn J; Borchert, Nicole S; Stöveken, Nadine; Bremer, Erhard; Wittmann, Christoph

2013-11-15

The stabilizing and function-preserving effects of ectoines have attracted considerable biotechnological interest up to industrial scale processes for their production. These rely on the release of ectoines from high-salinity-cultivated microbial producer cells upon an osmotic down-shock in rather complex processor configurations. There is growing interest in uncoupling the production of ectoines from the typical conditions required for their synthesis, and instead design strains that naturally release ectoines into the medium without the need for osmotic changes, since the use of high-salinity media in the fermentation process imposes notable constraints on the costs, design, and durability of fermenter systems. Here, we used a Corynebacterium glutamicum strain as a cellular chassis to establish a microbial cell factory for the biotechnological production of ectoines. The implementation of a mutant aspartokinase enzyme ensured efficient supply of L-aspartate-beta-semialdehyde, the precursor for ectoine biosynthesis. We further engineered the genome of the basic C. glutamicum strain by integrating a codon-optimized synthetic ectABCD gene cluster under expressional control of the strong and constitutive C. glutamicum tuf promoter. The resulting recombinant strain produced ectoine and excreted it into the medium; however, lysine was still found as a by-product. Subsequent inactivation of the L-lysine exporter prevented the undesired excretion of lysine while ectoine was still exported. Using the streamlined cell factory, a fed-batch process was established that allowed the production of ectoine with an overall productivity of 6.7 g L(-1) day(-1) under growth conditions that did not rely on the use of high-salinity media. The present study describes the construction of a stable microbial cell factory for recombinant production of ectoine. We successfully applied metabolic engineering strategies to optimize its synthetic production in the industrial workhorse C

Microalgal hydrogen production - A review.

PubMed

Khetkorn, Wanthanee; Rastogi, Rajesh P; Incharoensakdi, Aran; Lindblad, Peter; Madamwar, Datta; Pandey, Ashok; Larroche, Christian

2017-11-01

Bio-hydrogen from microalgae including cyanobacteria has attracted commercial awareness due to its potential as an alternative, reliable and renewable energy source. Photosynthetic hydrogen production from microalgae can be interesting and promising options for clean energy. Advances in hydrogen-fuel-cell technology may attest an eco-friendly way of biofuel production, since, the use of H 2 to generate electricity releases only water as a by-product. Progress in genetic/metabolic engineering may significantly enhance the photobiological hydrogen production from microalgae. Manipulation of competing metabolic pathways by modulating the certain key enzymes such as hydrogenase and nitrogenase may enhance the evolution of H 2 from photoautotrophic cells. Moreover, biological H 2 production at low operating costs is requisite for economic viability. Several photobioreactors have been developed for large-scale biomass and hydrogen production. This review highlights the recent technological progress, enzymes involved and genetic as well as metabolic engineering approaches towards sustainable hydrogen production from microalgae. Copyright © 2017 Elsevier Ltd. All rights reserved.

NIBBS-search for fast and accurate prediction of phenotype-biased metabolic systems.

PubMed

Schmidt, Matthew C; Rocha, Andrea M; Padmanabhan, Kanchana; Shpanskaya, Yekaterina; Banfield, Jill; Scott, Kathleen; Mihelcic, James R; Samatova, Nagiza F

2012-01-01

Understanding of genotype-phenotype associations is important not only for furthering our knowledge on internal cellular processes, but also essential for providing the foundation necessary for genetic engineering of microorganisms for industrial use (e.g., production of bioenergy or biofuels). However, genotype-phenotype associations alone do not provide enough information to alter an organism's genome to either suppress or exhibit a phenotype. It is important to look at the phenotype-related genes in the context of the genome-scale network to understand how the genes interact with other genes in the organism. Identification of metabolic subsystems involved in the expression of the phenotype is one way of placing the phenotype-related genes in the context of the entire network. A metabolic system refers to a metabolic network subgraph; nodes are compounds and edges labels are the enzymes that catalyze the reaction. The metabolic subsystem could be part of a single metabolic pathway or span parts of multiple pathways. Arguably, comparative genome-scale metabolic network analysis is a promising strategy to identify these phenotype-related metabolic subsystems. Network Instance-Based Biased Subgraph Search (NIBBS) is a graph-theoretic method for genome-scale metabolic network comparative analysis that can identify metabolic systems that are statistically biased toward phenotype-expressing organismal networks. We set up experiments with target phenotypes like hydrogen production, TCA expression, and acid-tolerance. We show via extensive literature search that some of the resulting metabolic subsystems are indeed phenotype-related and formulate hypotheses for other systems in terms of their role in phenotype expression. NIBBS is also orders of magnitude faster than MULE, one of the most efficient maximal frequent subgraph mining algorithms that could be adjusted for this problem. Also, the set of phenotype-biased metabolic systems output by NIBBS comes very close to

NIBBS-Search for Fast and Accurate Prediction of Phenotype-Biased Metabolic Systems

PubMed Central

Padmanabhan, Kanchana; Shpanskaya, Yekaterina; Banfield, Jill; Scott, Kathleen; Mihelcic, James R.; Samatova, Nagiza F.

2012-01-01

Understanding of genotype-phenotype associations is important not only for furthering our knowledge on internal cellular processes, but also essential for providing the foundation necessary for genetic engineering of microorganisms for industrial use (e.g., production of bioenergy or biofuels). However, genotype-phenotype associations alone do not provide enough information to alter an organism's genome to either suppress or exhibit a phenotype. It is important to look at the phenotype-related genes in the context of the genome-scale network to understand how the genes interact with other genes in the organism. Identification of metabolic subsystems involved in the expression of the phenotype is one way of placing the phenotype-related genes in the context of the entire network. A metabolic system refers to a metabolic network subgraph; nodes are compounds and edges labels are the enzymes that catalyze the reaction. The metabolic subsystem could be part of a single metabolic pathway or span parts of multiple pathways. Arguably, comparative genome-scale metabolic network analysis is a promising strategy to identify these phenotype-related metabolic subsystems. Network Instance-Based Biased Subgraph Search (NIBBS) is a graph-theoretic method for genome-scale metabolic network comparative analysis that can identify metabolic systems that are statistically biased toward phenotype-expressing organismal networks. We set up experiments with target phenotypes like hydrogen production, TCA expression, and acid-tolerance. We show via extensive literature search that some of the resulting metabolic subsystems are indeed phenotype-related and formulate hypotheses for other systems in terms of their role in phenotype expression. NIBBS is also orders of magnitude faster than MULE, one of the most efficient maximal frequent subgraph mining algorithms that could be adjusted for this problem. Also, the set of phenotype-biased metabolic systems output by NIBBS comes very close to

The JBEI quantitative metabolic modeling library (jQMM): a python library for modeling microbial metabolism

DOE PAGES

Birkel, Garrett W.; Ghosh, Amit; Kumar, Vinay S.; ...

2017-04-05

Modeling of microbial metabolism is a topic of growing importance in biotechnology. Mathematical modeling helps provide a mechanistic understanding for the studied process, separating the main drivers from the circumstantial ones, bounding the outcomes of experiments and guiding engineering approaches. Among different modeling schemes, the quantification of intracellular metabolic fluxes (i.e. the rate of each reaction in cellular metabolism) is of particular interest for metabolic engineering because it describes how carbon and energy flow throughout the cell. In addition to flux analysis, new methods for the effective use of the ever more readily available and abundant -omics data (i.e. transcriptomics,more » proteomics and metabolomics) are urgently needed. The jQMM library presented here provides an open-source, Python-based framework for modeling internal metabolic fluxes and leveraging other -omics data for the scientific study of cellular metabolism and bioengineering purposes. Firstly, it presents a complete toolbox for simultaneously performing two different types of flux analysis that are typically disjoint: Flux Balance Analysis and 13C Metabolic Flux Analysis. Moreover, it introduces the capability to use 13C labeling experimental data to constrain comprehensive genome-scale models through a technique called two-scale 13C Metabolic Flux Analysis (2S- 13C MFA). In addition, the library includes a demonstration of a method that uses proteomics data to produce actionable insights to increase biofuel production. Finally, the use of the jQMM library is illustrated through the addition of several Jupyter notebook demonstration files that enhance reproducibility and provide the capability to be adapted to the user's specific needs. jQMM will facilitate the design and metabolic engineering of organisms for biofuels and other chemicals, as well as investigations of cellular metabolism and leveraging -omics data. As an open source software project, we hope it

The JBEI quantitative metabolic modeling library (jQMM): a python library for modeling microbial metabolism

DOE Office of Scientific and Technical Information (OSTI.GOV)

Birkel, Garrett W.; Ghosh, Amit; Kumar, Vinay S.

Modeling of microbial metabolism is a topic of growing importance in biotechnology. Mathematical modeling helps provide a mechanistic understanding for the studied process, separating the main drivers from the circumstantial ones, bounding the outcomes of experiments and guiding engineering approaches. Among different modeling schemes, the quantification of intracellular metabolic fluxes (i.e. the rate of each reaction in cellular metabolism) is of particular interest for metabolic engineering because it describes how carbon and energy flow throughout the cell. In addition to flux analysis, new methods for the effective use of the ever more readily available and abundant -omics data (i.e. transcriptomics,more » proteomics and metabolomics) are urgently needed. The jQMM library presented here provides an open-source, Python-based framework for modeling internal metabolic fluxes and leveraging other -omics data for the scientific study of cellular metabolism and bioengineering purposes. Firstly, it presents a complete toolbox for simultaneously performing two different types of flux analysis that are typically disjoint: Flux Balance Analysis and 13C Metabolic Flux Analysis. Moreover, it introduces the capability to use 13C labeling experimental data to constrain comprehensive genome-scale models through a technique called two-scale 13C Metabolic Flux Analysis (2S- 13C MFA). In addition, the library includes a demonstration of a method that uses proteomics data to produce actionable insights to increase biofuel production. Finally, the use of the jQMM library is illustrated through the addition of several Jupyter notebook demonstration files that enhance reproducibility and provide the capability to be adapted to the user's specific needs. jQMM will facilitate the design and metabolic engineering of organisms for biofuels and other chemicals, as well as investigations of cellular metabolism and leveraging -omics data. As an open source software project, we hope it

Application of metabolic controls for the maximization of lipid production in semicontinuous fermentation.

PubMed

Xu, Jingyang; Liu, Nian; Qiao, Kangjian; Vogg, Sebastian; Stephanopoulos, Gregory

2017-07-03

Acetic acid can be generated through syngas fermentation, lignocellulosic biomass degradation, and organic waste anaerobic digestion. Microbial conversion of acetate into triacylglycerols for biofuel production has many advantages, including low-cost or even negative-cost feedstock and environmental benefits. The main issue stems from the dilute nature of acetate produced in such systems, which is costly to be processed on an industrial scale. To tackle this problem, we established an efficient bioprocess for converting dilute acetate into lipids, using the oleaginous yeast Yarrowia lipolytica in a semicontinuous system. The implemented design used low-strength acetic acid in both salt and acid forms as carbon substrate and a cross-filtration module for cell recycling. Feed controls for acetic acid and nitrogen based on metabolic models and online measurement of the respiratory quotient were used. The optimized process was able to sustain high-density cell culture using acetic acid of only 3% and achieved a lipid titer, yield, and productivity of 115 g/L, 0.16 g/g, and 0.8 g⋅L -1 ⋅h -1 , respectively. No carbon substrate was detected in the effluent stream, indicating complete utilization of acetate. These results represent a more than twofold increase in lipid production metrics compared with the current best-performing results using concentrated acetic acid as carbon feed.

Glucose metabolic flux distribution of Lactobacillus amylophilus during lactic acid production using kitchen waste saccharified solution.

PubMed

Liu, Jianguo; Wang, Qunhui; Zou, Hui; Liu, Yingying; Wang, Juan; Gan, Kemin; Xiang, Juan

2013-11-01

The (13) C isotope tracer method was used to investigate the glucose metabolic flux distribution and regulation in Lactobacillus amylophilus to improve lactic acid production using kitchen waste saccharified solution (KWSS). The results demonstrate that L. amylophilus is a homofermentative bacterium. In synthetic medium, 60.6% of the glucose entered the Embden-Meyerhof-Parnas (EMP) to produce lactic acid, whereas 36.4% of the glucose entered the pentose phosphate metabolic pathway (HMP). After solid-liquid separation of the KWSS, the addition of Fe(3+) during fermentation enhanced the NADPH production efficiency and increased the NADH content. The flux to the EMP was also effectively increased. Compared with the control (60.6% flux to EMP without Fe(3+) addition), the flux to the EMP with the addition of Fe(3+) (74.3%) increased by 23.8%. In the subsequent pyruvate metabolism, Fe(3+) also increased lactate dehydrogenase activity, and inhibited alcohol dehydrogenase, pyruvate dehydrogenase and pyruvate carboxylase, thereby increasing the lactic acid production to 9.03 g l(-1) , an increase of 8% compared with the control. All other organic acid by-products were lower than in the control. However, the addition of Zn(2+) showed an opposite effect, decreasing the lactic acid production. In conclusion it is feasible and effective means using GC-MS, isotope experiment and MATLAB software to integrate research the metabolic flux distribution of lactic acid bacteria, and the results provide the theoretical foundation for similar metabolic flux distribution. © 2013 The Authors. Microbial Biotechnology published by John Wiley & Sons Ltd and Society for Applied Microbiology.

Effects of rutin and buckwheat seeds on energy metabolism and methane production in dairy cows.

PubMed

Stoldt, Ann-Kathrin; Derno, Michael; Das, Gürbüz; Weitzel, Joachim M; Wolffram, Siegfried; Metges, Cornelia C

2016-03-01

Flavonoids are secondary plant metabolites with several health promoting effects. As dairy cows often suffer from metabolic imbalance and health problems, interest is growing in health improvements by plant substances such as flavonoids. Our group has recently shown that the flavonoids quercetin and rutin (a glucorhamnoside of quercetin) are bioavailable in cows when given via a duodenal fistula or orally, respectively, affect glucose metabolism, and have beneficial effects on liver health. Furthermore, flavonoids may reduce rumen methane production in vitro through their antibacterial properties. To test the hypothesis that rutin has effects on energy metabolism, methane production, and production performance in dairy cows, we fed rutin trihydrate at a dose of 100mg/kg of body weight to a group of 7 lactating dairy cows for 2 wk in a crossover design. In a second experiment, 2 cows were fed the same ration but were supplemented with buckwheat seeds (Fagopyrum tartaricum), providing rutin at a dose comparable to the first experiment. Two other cows receiving barley supplements were used as controls in a change-over mode. Blood samples were taken weekly and respiration measurements were performed at the end of each treatment. Supplementation of pure rutin, but not of rutin contained in buckwheat seeds, increased the plasma quercetin content. Methane production and milk yield and composition were not affected by rutin treatment in either form. Plasma glucose, β-hydroxybutyrate, and albumin were increased by pure rutin treatment, indicating a possible metabolic effect of rutin on energy metabolism of dairy cows. In addition, we did not show that in vivo ruminal methane production was reduced by rutin. In conclusion, we could not confirm earlier reports on in vitro methane reduction by rutin supplementation in dairy cows in established lactation. Copyright © 2016 American Dairy Science Association. Published by Elsevier Inc. All rights reserved.

Multi‐omic profiling ­of EPO‐producing Chinese hamster ovary cell panel reveals metabolic adaptation to heterologous protein production

PubMed Central

Ley, Daniel; Seresht, Ali Kazemi; Engmark, Mikael; Magdenoska, Olivera; Nielsen, Kristian Fog; Kildegaard, Helene Faustrup

2015-01-01

ABSTRACT Chinese hamster ovary (CHO) cells are the preferred production host for many therapeutic proteins. The production of heterologous proteins in CHO cells imposes a burden on the host cell metabolism and impact cellular physiology on a global scale. In this work, a multi‐omics approach was applied to study the production of erythropoietin (EPO) in a panel of CHO‐K1 cells under growth‐limited and unlimited conditions in batch and chemostat cultures. Physiological characterization of the EPO‐producing cells included global transcriptome analysis, targeted metabolome analysis, including intracellular pools of glycolytic intermediates, NAD(P)H/NAD(P)+, adenine nucleotide phosphates (ANP), and extracellular concentrations of sugars, organic acids, and amino acids. Potential impact of EPO expression on the protein secretory pathway was assessed at multiple stages using quantitative PCR (qPCR), reverse transcription PCR (qRT‐PCR), Western blots (WB), and global gene expression analysis to assess EPO gene copy numbers, EPO gene expression, intracellular EPO retention, and differentially expressed genes functionally related to secretory protein processing, respectively. We found no evidence supporting the existence of production bottlenecks in energy metabolism (i.e., glycolytic metabolites, NAD(P)H/NAD(P)+ and ANPs) in batch culture or in the secretory protein production pathway (i.e., gene dosage, transcription and post‐translational processing of EPO) in chemostat culture at specific productivities up to 5 pg/cell/day. Time‐course analysis of high‐ and low‐producing clones in chemostat culture revealed rapid adaptation of transcription levels of amino acid catabolic genes in favor of EPO production within nine generations. Interestingly, the adaptation was followed by an increase in specific EPO productivity. Biotechnol. Bioeng. 2015;112: 2373–2387. © 2015 The Authors. Biotechnology and Bioengineering Published by Wiley Periodicals, Inc. PMID

Thermocline deepening boosts ecosystem metabolism: evidence from a large-scale lake enclosure experiment simulating a summer storm.

PubMed

Giling, Darren P; Nejstgaard, Jens C; Berger, Stella A; Grossart, Hans-Peter; Kirillin, Georgiy; Penske, Armin; Lentz, Maren; Casper, Peter; Sareyka, Jörg; Gessner, Mark O

2017-04-01

Extreme weather events can pervasively influence ecosystems. Observations in lakes indicate that severe storms in particular can have pronounced ecosystem-scale consequences, but the underlying mechanisms have not been rigorously assessed in experiments. One major effect of storms on lakes is the redistribution of mineral resources and plankton communities as a result of abrupt thermocline deepening. We aimed at elucidating the importance of this effect by mimicking in replicated large enclosures (each 9 m in diameter, ca. 20 m deep, ca. 1300 m 3 in volume) a mixing event caused by a severe natural storm that was previously observed in a deep clear-water lake. Metabolic rates were derived from diel changes in vertical profiles of dissolved oxygen concentrations using a Bayesian modelling approach, based on high-frequency measurements. Experimental thermocline deepening stimulated daily gross primary production (GPP) in surface waters by an average of 63% for >4 weeks even though thermal stratification re-established within 5 days. Ecosystem respiration (ER) was tightly coupled to GPP, exceeding that in control enclosures by 53% over the same period. As GPP responded more strongly than ER, net ecosystem productivity (NEP) of the entire water column was also increased. These protracted increases in ecosystem metabolism and autotrophy were driven by a proliferation of inedible filamentous cyanobacteria released from light and nutrient limitation after they were entrained from below the thermocline into the surface water. Thus, thermocline deepening by a single severe storm can induce prolonged responses of lake ecosystem metabolism independent of other storm-induced effects, such as inputs of terrestrial materials by increased catchment run-off. This highlights that future shifts in frequency, severity or timing of storms are an important component of climate change, whose impacts on lake thermal structure will superimpose upon climate trends to influence algal

A mixed-integer linear programming approach to the reduction of genome-scale metabolic networks.

PubMed

Röhl, Annika; Bockmayr, Alexander

2017-01-03

Constraint-based analysis has become a widely used method to study metabolic networks. While some of the associated algorithms can be applied to genome-scale network reconstructions with several thousands of reactions, others are limited to small or medium-sized models. In 2015, Erdrich et al. introduced a method called NetworkReducer, which reduces large metabolic networks to smaller subnetworks, while preserving a set of biological requirements that can be specified by the user. Already in 2001, Burgard et al. developed a mixed-integer linear programming (MILP) approach for computing minimal reaction sets under a given growth requirement. Here we present an MILP approach for computing minimum subnetworks with the given properties. The minimality (with respect to the number of active reactions) is not guaranteed by NetworkReducer, while the method by Burgard et al. does not allow specifying the different biological requirements. Our procedure is about 5-10 times faster than NetworkReducer and can enumerate all minimum subnetworks in case there exist several ones. This allows identifying common reactions that are present in all subnetworks, and reactions appearing in alternative pathways. Applying complex analysis methods to genome-scale metabolic networks is often not possible in practice. Thus it may become necessary to reduce the size of the network while keeping important functionalities. We propose a MILP solution to this problem. Compared to previous work, our approach is more efficient and allows computing not only one, but even all minimum subnetworks satisfying the required properties.

Shikimic Acid Production in Escherichia coli: From Classical Metabolic Engineering Strategies to Omics Applied to Improve Its Production.

PubMed

Martínez, Juan Andrés; Bolívar, Francisco; Escalante, Adelfo

2015-01-01

Shikimic acid (SA) is an intermediate of the SA pathway that is present in bacteria and plants. SA has gained great interest because it is a precursor in the synthesis of the drug oseltamivir phosphate (OSF), an efficient inhibitor of the neuraminidase enzyme of diverse seasonal influenza viruses, the avian influenza virus H5N1, and the human influenza virus H1N1. For the purposes of OSF production, SA is extracted from the pods of Chinese star anise plants (Illicium spp.), yielding up to 17% of SA (dry basis content). The high demand for OSF necessary to manage a major influenza outbreak is not adequately met by industrial production using SA from plants sources. As the SA pathway is present in the model bacteria Escherichia coli, several "intuitive" metabolically engineered strains have been applied for its successful overproduction by biotechnological processes, resulting in strains producing up to 71 g/L of SA, with high conversion yields of up to 0.42 (mol SA/mol Glc), in both batch and fed-batch cultures using complex fermentation broths, including glucose as a carbon source and yeast extract. Global transcriptomic analyses have been performed in SA-producing strains, resulting in the identification of possible key target genes for the design of a rational strain improvement strategy. Because possible target genes are involved in the transport, catabolism, and interconversion of different carbon sources and metabolic intermediates outside the central carbon metabolism and SA pathways, as genes involved in diverse cellular stress responses, the development of rational cellular strain improvement strategies based on omics data constitutes a challenging task to improve SA production in currently overproducing engineered strains. In this review, we discuss the main metabolic engineering strategies that have been applied for the development of efficient SA-producing strains, as the perspective of omics analysis has focused on further strain improvement for the

Shikimic Acid Production in Escherichia coli: From Classical Metabolic Engineering Strategies to Omics Applied to Improve Its Production

PubMed Central

Martínez, Juan Andrés; Bolívar, Francisco; Escalante, Adelfo

2015-01-01

Shikimic acid (SA) is an intermediate of the SA pathway that is present in bacteria and plants. SA has gained great interest because it is a precursor in the synthesis of the drug oseltamivir phosphate (OSF), an efficient inhibitor of the neuraminidase enzyme of diverse seasonal influenza viruses, the avian influenza virus H5N1, and the human influenza virus H1N1. For the purposes of OSF production, SA is extracted from the pods of Chinese star anise plants (Illicium spp.), yielding up to 17% of SA (dry basis content). The high demand for OSF necessary to manage a major influenza outbreak is not adequately met by industrial production using SA from plants sources. As the SA pathway is present in the model bacteria Escherichia coli, several “intuitive” metabolically engineered strains have been applied for its successful overproduction by biotechnological processes, resulting in strains producing up to 71 g/L of SA, with high conversion yields of up to 0.42 (mol SA/mol Glc), in both batch and fed-batch cultures using complex fermentation broths, including glucose as a carbon source and yeast extract. Global transcriptomic analyses have been performed in SA-producing strains, resulting in the identification of possible key target genes for the design of a rational strain improvement strategy. Because possible target genes are involved in the transport, catabolism, and interconversion of different carbon sources and metabolic intermediates outside the central carbon metabolism and SA pathways, as genes involved in diverse cellular stress responses, the development of rational cellular strain improvement strategies based on omics data constitutes a challenging task to improve SA production in currently overproducing engineered strains. In this review, we discuss the main metabolic engineering strategies that have been applied for the development of efficient SA-producing strains, as the perspective of omics analysis has focused on further strain improvement for

Global metabolic rewiring for improved CO2 fixation and chemical production in cyanobacteria

NASA Astrophysics Data System (ADS)

Kanno, Masahiro; Carroll, Austin L.; Atsumi, Shota

2017-03-01

Cyanobacteria have attracted much attention as hosts to recycle CO2 into valuable chemicals. Although cyanobacteria have been engineered to produce various compounds, production efficiencies are too low for commercialization. Here we engineer the carbon metabolism of Synechococcus elongatus PCC 7942 to improve glucose utilization, enhance CO2 fixation and increase chemical production. We introduce modifications in glycolytic pathways and the Calvin Benson cycle to increase carbon flux and redirect it towards carbon fixation. The engineered strain efficiently uses both CO2 and glucose, and produces 12.6 g l-1 of 2,3-butanediol with a rate of 1.1 g l-1 d-1 under continuous light conditions. Removal of native regulation enables carbon fixation and 2,3-butanediol production in the absence of light. This represents a significant step towards industrial viability and an excellent example of carbon metabolism plasticity.

A Multi-scale Computational Platform to Mechanistically Assess the Effect of Genetic Variation on Drug Responses in Human Erythrocyte Metabolism

PubMed Central

Bordbar, Aarash; Palsson, Bernhard O.

2016-01-01

Progress in systems medicine brings promise to addressing patient heterogeneity and individualized therapies. Recently, genome-scale models of metabolism have been shown to provide insight into the mechanistic link between drug therapies and systems-level off-target effects while being expanded to explicitly include the three-dimensional structure of proteins. The integration of these molecular-level details, such as the physical, structural, and dynamical properties of proteins, notably expands the computational description of biochemical network-level properties and the possibility of understanding and predicting whole cell phenotypes. In this study, we present a multi-scale modeling framework that describes biological processes which range in scale from atomistic details to an entire metabolic network. Using this approach, we can understand how genetic variation, which impacts the structure and reactivity of a protein, influences both native and drug-induced metabolic states. As a proof-of-concept, we study three enzymes (catechol-O-methyltransferase, glucose-6-phosphate dehydrogenase, and glyceraldehyde-3-phosphate dehydrogenase) and their respective genetic variants which have clinically relevant associations. Using all-atom molecular dynamic simulations enables the sampling of long timescale conformational dynamics of the proteins (and their mutant variants) in complex with their respective native metabolites or drug molecules. We find that changes in a protein’s structure due to a mutation influences protein binding affinity to metabolites and/or drug molecules, and inflicts large-scale changes in metabolism. PMID:27467583

Metabolic Engineering toward Sustainable Production of Nylon-6.

PubMed

Turk, Stefan C H J; Kloosterman, Wigard P; Ninaber, Dennis K; Kolen, Karin P A M; Knutova, Julia; Suir, Erwin; Schürmann, Martin; Raemakers-Franken, Petronella C; Müller, Monika; de Wildeman, Stefaan M A; Raamsdonk, Leonie M; van der Pol, Ruud; Wu, Liang; Temudo, Margarida F; van der Hoeven, Rob A M; Akeroyd, Michiel; van der Stoel, Roland E; Noorman, Henk J; Bovenberg, Roel A L; Trefzer, Axel C

2016-01-15

Nylon-6 is a bulk polymer used for many applications. It consists of the non-natural building block 6-aminocaproic acid, the linear form of caprolactam. Via a retro-synthetic approach, two synthetic pathways were identified for the fermentative production of 6-aminocaproic acid. Both pathways require yet unreported novel biocatalytic steps. We demonstrated proof of these bioconversions by in vitro enzyme assays with a set of selected candidate proteins expressed in Escherichia coli. One of the biosynthetic pathways starts with 2-oxoglutarate and contains bioconversions of the ketoacid elongation pathway known from methanogenic archaea. This pathway was selected for implementation in E. coli and yielded 6-aminocaproic acid at levels up to 160 mg/L in lab-scale batch fermentations. The total amount of 6-aminocaproic acid and related intermediates generated by this pathway exceeded 2 g/L in lab-scale fed-batch fermentations, indicating its potential for further optimization toward large-scale sustainable production of nylon-6.

Comparison of Lipid Accumulation Product Index with Body Mass Index and Waist Circumference as a Predictor of Metabolic Syndrome in Indian Population.