New light on ancient enzymes - in vitro CO2 Fixation by Pyruvate Synthase of Desulfovibrio africanus and Sulfolobus acidocaldarius.

Two variants of the enzyme family pyruvate:ferredoxin oxidoreductase (PFOR), derived from the anaerobic sulfate-reducing bacterium Desulfovibrio africanus and the extremophilic crenarchaeon Sulfolobus acidocaldarius, respectively, were evaluated for their capacity to fixate CO2 in vitro. PFOR reversibly catalyzes the conversion of acetyl-CoA and CO2 to pyruvate using ferredoxin as redox partner. The oxidative decarboxylation of pyruvate is thermodynamically strongly favored, and most previous studies only considered the oxidative direction of the enzyme. To assay the pyruvate synthase function of PFOR during reductive carboxylation of acetyl-CoA is more challenging and requires to maintain the reaction far from equilibrium. For this purpose, a biochemical assay was established where low-potential electrons were introduced by photochemical reduction of EDTA/deazaflavin and the generated pyruvate was trapped by chemical derivatization with semicarbazide. The product of CO2 fixation could be detected as pyruvate semicarbazone by HPLC-MS. In a combinatorial approach, both PFORs were tested with ferredoxins from different sources. The pyruvate semicarbazone product could be detected with low-potential ferredoxins of the green sulfur bacterium Chlorobium tepidum and of S. acidocaldarius whereas CO2 fixation was not supported by the native ferredoxin of D. africanus. Methylviologen as an artificial electron carrier also allowed CO2 fixation. For both enzymes, the results are the first demonstration of CO2 fixation in vitro. Both enzymes exhibited high stability in the presence of oxygen during purification and storage. In conclusion, the employed PFOR enzymes in combination with non-native ferredoxin cofactors might be promising candidates for further incorporation in biocatalytic CO2 conversion. ENZYMES: EC1.2.7.1. Pyruvate:Ferredoxin Oxidoreductase.

OptMDFpathway: Identification of metabolic pathways with maximal thermodynamic driving force and its application for analyzing the endogenous CO2 fixation potential of Escherichia coli.

Constraint-based modeling techniques have become a standard tool for the in silico analysis of metabolic networks. To further improve their accuracy, recent methodological developments focused on integration of thermodynamic information in metabolic models to assess the feasibility of flux distributions by thermodynamic driving forces. Here we present OptMDFpathway, a method that extends the recently proposed framework of Max-min Driving Force (MDF) for thermodynamic pathway analysis. Given a metabolic network model, OptMDFpathway identifies both the optimal MDF for a desired phenotypic behavior as well as the respective pathway itself that supports the optimal driving force. OptMDFpathway is formulated as a mixed-integer linear program and is applicable to genome-scale metabolic networks. As an important theoretical result, we also show that there exists always at least one elementary mode in the network that reaches the maximal MDF. We employed our new approach to systematically identify all substrate-product combinations in Escherichia coli where product synthesis allows for concomitant net CO2 assimilation via thermodynamically feasible pathways. Although biomass synthesis cannot be coupled to net CO2 fixation in E. coli we found that as many as 145 of the 949 cytosolic carbon metabolites contained in the genome-scale model iJO1366 enable net CO2 incorporation along thermodynamically feasible pathways with glycerol as substrate and 34 with glucose. The most promising products in terms of carbon assimilation yield and thermodynamic driving forces are orotate, aspartate and the C4-metabolites of the tricarboxylic acid cycle. We also identified thermodynamic bottlenecks frequently limiting the maximal driving force of the CO2-fixing pathways. Our results indicate that heterotrophic organisms like E. coli hold a possibly underestimated potential for CO2 assimilation which may complement existing biotechnological approaches for capturing CO2. Furthermore, we envision that the developed OptMDFpathway approach can be used for many other applications within the framework of constrained-based modeling and for rational design of metabolic networks.

When Do Two-Stage Processes Outperform One-Stage Processes?

Apart from product yield and titer, volumetric productivity is a key performance indicator for many biotechnological processes. Due to the inherent trade-off between the production of biomass as catalyst and of the actual target product, yield and volumetric productivity cannot be optimized simultaneously. Therefore, in combination with genetic techniques for dynamic regulation of metabolic fluxes, two-stage fermentations (TSFs) with separated growth and production phase have recently gained much interest because of their potential to improve the productivity of bioprocesses while still allowing high product yields. However, despite some successful case studies, so far it has not been discussed and analyzed systematically whether or under which conditions a TSF guarantees superior productivity compared to one-stage fermentation (OSF). In this study, we use mathematical models to demonstrate that the volumetric productivity of a TSF is not automatically better than of a corresponding OSF. Our analysis reveals that the sharp decrease of the specific substrate uptake rate usually observed in (non-growth) production phases severely impacts the volumetric productivity and thus raises a big challenge for designing competitive TSF processes. We discuss possible approaches such as enforced ATP wasting to improve substrate utilization rates in the production phase by which TSF processes can become superior to OSF. We also analyze additional factors influencing the relative performance of OSF and TSF and show that OSF processes can be more appropriate if a high product yield is an economic constraint. In conclusion, a careful assessment of the trade-offs between substrate uptake rates, yields, and productivity is necessary when deciding for OSF vs. TSF processes.

Use of CellNetAnalyzer in biotechnology and metabolic engineering.

Mathematical models of the cellular metabolism have become an essential tool for the optimization of biotechnological processes. They help to obtain a systemic understanding of the metabolic processes in the used microorganisms and to find suitable genetic modifications maximizing the production performance. In particular, methods of stoichiometric and constraint-based modeling are frequently used in the context of metabolic and bioprocess engineering. Since metabolic networks can be complex and comprise hundreds or even thousands of metabolites and reactions, dedicated software tools are required for an efficient analysis. One such software suite is CellNetAnalyzer, a MATLAB package providing, among others, various methods for analyzing stoichiometric and constraint-based metabolic models. CellNetAnalyzer can be used via command-line based operations or via a graphical user interface with embedded network visualizations. Herein we will present key functionalities of CellNetAnalyzer for applications in biotechnology and metabolic engineering and thereby review constraint-based modeling techniques such as metabolic flux analysis, flux balance analysis, flux variability analysis, metabolic pathway analysis (elementary flux modes) and methods for computational strain design.

EColiCore2: a reference network model of the central metabolism of Escherichia coli and relationships to its genome-scale parent model.

Genome-scale metabolic modeling has become an invaluable tool to analyze properties and capabilities of metabolic networks and has been particularly successful for the model organism Escherichia coli. However, for several applications, smaller metabolic (core) models are needed. Using a recently introduced reduction algorithm and the latest E. coli genome-scale reconstruction iJO1366, we derived EColiCore2, a model of the central metabolism of E. coli. EColiCore2 is a subnetwork of iJO1366 and preserves predefined phenotypes including optimal growth on different substrates. The network comprises 486 metabolites and 499 reactions, is accessible for elementary-modes analysis and can, if required, be further compressed to a network with 82 reactions and 54 metabolites having an identical solution space as EColiCore2. A systematic comparison of EColiCore2 with its genome-scale parent model iJO1366 reveals that several key properties (flux ranges, reaction essentialities, production envelopes) of the central metabolism are preserved in EColiCore2 while it neglects redundancies along biosynthetic routes. We also compare calculated metabolic engineering strategies in both models and demonstrate, as a general result, how intervention strategies found in a core model allow the identification of valid strategies in a genome-scale model. Overall, EColiCore2 holds promise to become a reference model of E. coli's central metabolism.

Manipulation of the ATP pool as a tool for metabolic engineering.

Cofactor engineering has been long identified as a valuable tool for metabolic engineering. Besides interventions targeting the pools of redox cofactors, many studies addressed the adenosine pools of microorganisms. In this mini-review, we discuss interventions that manipulate the availability of ATP with a special focus on ATP wasting strategies. We discuss the importance to fine-tune the ATP yield along a production pathway to balance process performance parameters like product yield and volumetric productivity.

Enforced ATP futile cycling increases specific productivity and yield of anaerobic lactate production in Escherichia coli.

The manipulation of cofactor pools such as ATP or NAD(P)H has for long been recognized as key targets for metabolic engineering of microorganisms to improve yields and productivities of biotechnological processes. Several works in the past have shown that enforcing ATP futile cycling may enhance the synthesis of certain products under aerobic conditions. However, case studies demonstrating that ATP wasting may also have beneficial effects for anaerobic production processes are scarce. Taking lactic acid as an economically relevant product, we demonstrate that induction of ATP futile cycling in Escherichia coli leads to increased yields and specific production rates under anaerobic conditions, even in the case where lactate is already produced with high yields. Specifically, we constructed a high lactate producer strain KBM10111 (= MG1655 ΔadhE::Cam ΔackA-pta) and implemented an IPTG-inducible overexpression of ppsA encoding for PEP synthase which, together with pyruvate kinase, gives rise to an ATP consuming cycle. Under induction of ppsA, KBM10111 exhibits a 25% higher specific lactate productivity as well as an 8% higher lactate yield. Furthermore, the specific substrate uptake rate was increased by 14%. However, trade-offs between specific and volumetric productivities must be considered when ATP wasting strategies are used to shift substrate conversion from biomass to product synthesis and we discuss potential solutions to design optimal processes. In summary, enforced ATP futile cycling has great potential to optimize a variety of production processes and our study demonstrates that this holds true also for anaerobic processes.

The avian cell line AGE1.CR.pIX characterized by metabolic flux analysis.

BACKGROUND: In human vaccine manufacturing some pathogens such as Modified Vaccinia Virus Ankara, measles, mumps virus as well as influenza viruses are still produced on primary material derived from embryonated chicken eggs. Processes depending on primary cell culture, however, are difficult to adapt to modern vaccine production. Therefore, we derived previously a continuous suspension cell line, AGE1.CR.pIX, from muscovy duck and established chemically-defined media for virus propagation. RESULTS: To better understand vaccine production processes, we developed a stoichiometric model of the central metabolism of AGE1.CR.pIX cells and applied flux variability and metabolic flux analysis. Results were compared to literature dealing with mammalian and insect cell culture metabolism focusing on the question whether cultured avian cells differ in metabolism. Qualitatively, the observed flux distribution of this avian cell line was similar to distributions found for mammalian cell lines (e.g. CHO, MDCK cells). In particular, glucose was catabolized inefficiently and glycolysis and TCA cycle seem to be only weakly connected. CONCLUSIONS: A distinguishing feature of the avian cell line is that glutaminolysis plays only a minor role in energy generation and production of precursors, resulting in low extracellular ammonia concentrations. This metabolic flux study is the first for a continuous avian cell line. It provides a basis for further metabolic analyses to exploit the biotechnological potential of avian and vertebrate cell lines and to develop specific optimized cell culture processes, e.g. vaccine production processes.

In silico profiling of Escherichia coli and Saccharomyces cerevisiae as terpenoid factories.

BACKGROUND: Heterologous microbial production of rare plant terpenoids of medicinal or industrial interest is attracting more and more attention but terpenoid yields are still low. Escherichia coli and Saccharomyces cerevisiae are the most widely used heterologous hosts; a direct comparison of both hosts based on experimental data is difficult though. Hence, the terpenoid pathways of E. coli (via 1-deoxy-D-xylulose 5-phosphate, DXP) and S. cerevisiae (via mevalonate, MVA), the impact of the respective hosts metabolism as well as the impact of different carbon sources were compared in silico by means of elementary mode analysis. The focus was set on the yield of isopentenyl diphosphate (IPP), the general terpenoid precursor, to identify new metabolic engineering strategies for an enhanced terpenoid yield. RESULTS: Starting from the respective precursor metabolites of the terpenoid pathways (pyruvate and glyceraldehyde-3-phosphate for the DXP pathway and acetyl-CoA for the MVA pathway) and considering only carbon stoichiometry, the two terpenoid pathways are identical with respect to carbon yield. However, with glucose as substrate, the MVA pathway has a lower potential to supply terpenoids in high yields than the DXP pathway if the formation of the required precursors is taken into account, due to the carbon loss in the formation of acetyl-CoA. This maximum yield is further reduced in both hosts when the required energy and reduction equivalents are considered. Moreover, the choice of carbon source (glucose, xylose, ethanol or glycerol) has an effect on terpenoid yield with non-fermentable carbon sources being more promising. Both hosts have deficiencies in energy and redox equivalents for high yield terpenoid production leading to new overexpression strategies (heterologous enzymes/pathways) for an enhanced terpenoid yield. Finally, several knockout strategies are identified using constrained minimal cut sets enforcing a coupling of growth to a terpenoid yield which is higher than any yield published in scientific literature so far. CONCLUSIONS: This study provides for the first time a comprehensive and detailed in silico comparison of the most prominent heterologous hosts E. coli and S. cerevisiae as terpenoid factories giving an overview on several promising metabolic engineering strategies paving the way for an enhanced terpenoid yield.

Stepwise reduction of the culture redox potential allows the analysis of microaerobic metabolism and photosynthetic membrane synthesis in Rhodospirillum rubrum.

Bacterial growth under oxygen-limited (microaerobic) conditions is often accompanied by phenomena of great interest for fundamental research and industrial application. The microaerobic lifestyle of anoxygenic photosynthetic bacteria like Rhodospirillum rubrum harbors such a phenomenon, as it allows the formation of photosynthetic membranes and related interesting products without light. However, due to the technical difficulties in process control of microaerobic cultivations and the limited sensitivity of available oxygen sensors, the analysis of microaerobic growth and physiology is still underrepresented in current research. The main focus of the present study was to establish an experimental set-up for the systematic study of physiological processes, associated with the growth of R. rubrum under microaerobic conditions in the dark. For this purpose, we introduce a robust and reliable microaerobic process control strategy, which applies the culture redox potential (CRP) for assessing different degrees of oxygen limitation in bioreactor cultivations. To describe the microaerobic growth behavior of R. rubrum cultures for each of these defined CRP reduction steps, basic growth parameters were experimentally determined. Flux variability analysis provided an insight into the metabolic activity of the TCA cycle and implied its connection to the respiratory capacity of the cells. In this context, our results suggest that microaerobic growth of R. rubrum can be described as an oxygen-activated cooperative mechanism. The present study thus contributes to the investigation of metabolic and regulatory events responsible for the redox-sensitive formation of photosynthetic membranes in facultative photosynthetic bacteria. Furthermore, the introduced microaerobic cultivation setup should be generally applicable for any microbial system of interest which can be cultivated in common stirred-tank bioreactors.

Metabolic network modeling of redox balancing and biohydrogen production in purple nonsulfur bacteria.

BACKGROUND: Purple nonsulfur bacteria (PNSB) are facultative photosynthetic bacteria and exhibit an extremely versatile metabolism. A central focus of research on PNSB dealt with the elucidation of mechanisms by which they manage to balance cellular redox under diverse conditions, in particular under photoheterotrophic growth. RESULTS: Given the complexity of the central metabolism of PNSB, metabolic modeling becomes crucial for an integrated analysis of the accumulated biological knowledge. We reconstructed a stoichiometric model capturing the central metabolism of three important representatives of PNSB (Rhodospirillum rubrum, Rhodobacter sphaeroides and Rhodopseudomonas palustris). Using flux variability analysis, the model reveals key metabolic constraints related to redox homeostasis in these bacteria. With the help of the model we can (i) give quantitative explanations for non-intuitive, partially species-specific phenomena of photoheterotrophic growth of PNSB, (ii) reproduce various quantitative experimental data, and (iii) formulate several new hypotheses. For example, model analysis of photoheterotrophic growth reveals that--despite a large number of utilizable catabolic pathways--substrate-specific biomass and CO2 yields are fixed constraints, irrespective of the assumption of optimal growth. Furthermore, our model explains quantitatively why a CO2 fixing pathway such as the Calvin cycle is required by PNSB for many substrates (even if CO2 is released). We also analyze the role of other pathways potentially involved in redox metabolism and how they affect quantitatively the required capacity of the Calvin cycle. Our model also enables us to discriminate between different acetate assimilation pathways that were proposed recently for R. sphaeroides and R. rubrum, both lacking the isocitrate lyase. Finally, we demonstrate the value of the metabolic model also for potential biotechnological applications: we examine the theoretical capabilities of PNSB for photoheterotrophic hydrogen production and identify suitable genetic interventions to increase the hydrogen yield. CONCLUSIONS: Taken together, the metabolic model (i) explains various redox-related phenomena of the versatile metabolism of PNSB, (ii) delivers new hypotheses on the operation and relevance of several metabolic pathways, and (iii) holds significant potential as a tool for rational metabolic engineering of PNSB in biotechnological applications.

Computing complex metabolic intervention strategies using constrained minimal cut sets.

The model-driven search for gene deletion strategies that increase the production performance of microorganisms is an essential part of metabolic engineering. One theoretical approach is based on Minimal Cut Sets (MCSs) which are minimal sets of knockouts disabling the operation of a specified set of target elementary modes. A limitation of the current approach is that MCSs can induce side effects disabling also desired functionalities. We, therefore, generalize MCSs to Constrained MCSs (cMCSs) allowing for the additional definition of a set of desired modes of which a minimum number must be preserved. Exemplarily for ethanol production by Escherichia coli, we demonstrate that this approach offers enormous flexibility in defining and solving knockout problems. Moreover, many existing methods can be reformulated as special cMCS problems. The cMCSs approach allows systematic enumeration of all equivalent gene deletion combinations and also helps to determine robust knockout strategies for coupled product and biomass synthesis.

CASOP: a computational approach for strain optimization aiming at high productivity.

The identification of suitable intervention strategies increasing the productivity of microorganisms is a central issue in metabolic engineering. Here, we introduce a computational framework for strain optimization based on reaction importance measures derived from weighted elementary modes. The objective is to shift the natural flux distribution to synthesis of the desired product with high production rates thereby retaining the ability of the host organism to produce biomass precursors. The stoichiometric approach allows consideration of regulatory/operational constraints and takes product yield and network capacity--the two major determinants of (specific) productivity--explicitly into account. The relative contribution of each reaction to yield and network capacity and thus productivity is estimated by analyzing the spectrum of available conversion routes (elementary modes). A result of our procedure is a reaction ranking suggesting knockout and overexpression candidates. Moreover, we show that the methodology allows for the evaluation of cofactor and co-metabolite requirements in conjunction with product synthesis. We illustrate the proposed method by studying the overproduction of succinate and lactate by Escherichia coli. The metabolic engineering strategies identified in silico resemble existing mutant strains designed for the synthesis of the respective products. Additionally, some non-intuitive intervention strategies are revealed.

A general approach for sample size and power calculations based on the Haseman-Elston method.

Risch and Zhang (1995; Science 268: 1584-9) reported a simple sample size and power calculation approach for the Haseman-Elston method and based their computations on the null hypothesis of no genetic effect. We argue that the more reasonable null hypothesis is that of no recombination. For this null hypothesis, we provide a general approach for sample size and power calculations within the Haseman-Elston framework. We demonstrate the validity of our approach in a Monte-Carlo simulation study and illustrate the differences using data from published segregation analyses on body weight and heritability estimates on carotid artery artherosclerotic lesions.

SNPtoGO: characterizing SNPs by enriched GO terms.

UNLABELLED: For the analysis of complex polygenic diseases, one does not expect all patients to share the same disease-associated alleles. Not even will disease-causing variations be assigned to the identical sets of genes between patients. However, one does expect overlaps in the sets of genes that are involved and even more so in their assigned molecular processes. Furthermore, the assignment of single nucleotide polymorphisms (SNPs) to genes is highly ambiguous for intergenic SNPs. The tool presented here hence adds external information, i.e. GeneOntology (GO) terms (Gene Ontology Consortium), to the analysis of SNP data. AVAILABILITY: A web interface and source code are offered at https://webtools.imbs.uni-luebeck.de/snptogo