The Essence of Metabolic Engineering

Expanding chemistry through in vitro and in vivo biocatalysis

Living systems contain a vast network of metabolic reactions, providing a wealth of enzymes and cells as potential biocatalysts for chemical processes. The properties of protein and cell biocatalysts-high selectivity, the ability to control reaction sequence and operation in environmentally benign conditions-offer approaches to produce molecules at high efficiency while lowering the cost and environmental impact of industrial chemistry. Furthermore, biocatalysis offers the opportunity to generate chemical structures and functions that may be inaccessible to chemical synthesis. Here we consider developments in enzymes, biosynthetic pathways and cellular engineering that enable their use in catalysis for new chemistry and beyond.

State-of-the-art in engineering small molecule biosensors and their applications in metabolic engineering

Genetically encoded biosensors are crucial for enhancing our understanding of how molecules regulate biological systems. Small molecule biosensors, in particular, help us understand the interaction between chemicals and biological processes. They also accelerate metabolic engineering by increasing screening throughput and eliminating the need for sample preparation through traditional chemical analysis. Additionally, they offer significantly higher spatial and temporal resolution in cellular analyte measurements. In this review, we discuss recent progress in in vivo biosensors and control systems-biosensor-based controllers-for metabolic engineering. We also specifically explore protein-based biosensors that utilize less commonly exploited signaling mechanisms, such as protein stability and induced degradation, compared to more prevalent transcription factor and allosteric regulation mechanism. We propose that these lesser-used mechanisms will be significant for engineering eukaryotic systems and slower-growing prokaryotic systems where protein turnover may facilitate more rapid and reliable measurement and regulation of the current cellular state. Lastly, we emphasize the utilization of cutting-edge and state-of-the-art techniques in the development of protein-based biosensors, achieved through rational design, directed evolution, and collaborative approaches.

Metabolic regulatory network kinetic modeling with multiple isotopic tracers for iPSCs

The rapidly expanding market for regenerative medicines and cell therapies highlights the need to advance the understanding of cellular metabolisms and improve the prediction of cultivation production process for human induced pluripotent stem cells (iPSCs). In this paper, a metabolic kinetic model was developed to characterize the underlying mechanisms of iPSC culture process, which can predict cell response to environmental perturbation and support process control. This model focuses on the central carbon metabolic network, including glycolysis, pentose phosphate pathway, tricarboxylic acid cycle, and amino acid metabolism, which plays a crucial role to support iPSC proliferation. Heterogeneous measures of extracellular metabolites and multiple isotopic tracers collected under multiple conditions were used to learn metabolic regulatory mechanisms. Systematic cross-validation confirmed the model's performance in terms of providing reliable predictions on cellular metabolism and culture process dynamics under various culture conditions. Thus, the developed mechanistic kinetic model can support process control strategies to strategically select optimal cell culture conditions at different times, ensure cell product functionality, and facilitate large-scale manufacturing of regenerative medicines and cell therapies.

Integration of graph neural networks and genome-scale metabolic models for predicting gene essentiality

Genome-scale metabolic models are powerful tools for understanding cellular physiology. Flux balance analysis (FBA), in particular, is an optimization-based approach widely employed for predicting metabolic phenotypes. In model microbes such as Escherichia coli, FBA has been successful at predicting essential genes, i.e. those genes that impair survival when deleted. A central assumption in this approach is that both wild type and deletion strains optimize the same fitness objective. Although the optimality assumption may hold for the wild type metabolic network, deletion strains are not subject to the same evolutionary pressures and knock-out mutants may steer their metabolism to meet other objectives for survival. Here, we present FlowGAT, a hybrid FBA-machine learning strategy for predicting essentiality directly from wild type metabolic phenotypes. The approach is based on graph-structured representation of metabolic fluxes predicted by FBA, where nodes correspond to enzymatic reactions and edges quantify the propagation of metabolite mass flow between a reaction and its neighbours. We integrate this information into a graph neural network that can be trained on knock-out fitness assay data. Comparisons across different model architectures reveal that FlowGAT predictions for E. coli are close to those of FBA for several growth conditions. This suggests that essentiality of enzymatic genes can be predicted by exploiting the inherent network structure of metabolism. Our approach demonstrates the benefits of combining the mechanistic insights afforded by genome-scale models with the ability of deep learning to infer patterns from complex datasets.

Protein production dynamics and physiological adaptation of recombinant Komagataella phaffii at near-zero growth rates

BACKGROUND

Specific productivity (qP) in yeast correlates with growth, typically peaking at intermediate or maximum specific growth rates (μ). Understanding the factors limiting productivity at extremely low μ might reveal decoupling strategies, but knowledge of production dynamics and physiology in such conditions is scarce. Retentostats, a type of continuous cultivation, enable the well-controlled transition to near-zero µ through the combined retention of biomass and limited substrate supply. Recombinant Komagataella phaffii (syn Pichia pastoris) secreting a bivalent single domain antibody (VHH) was cultivated in aerobic, glucose-limited retentostats to investigate recombinant protein production dynamics and broaden our understanding of relevant physiological adaptations at near-zero growth conditions.

RESULTS

By the end of the retentostat cultivation, doubling times of approx. two months were reached, corresponding to µ = 0.00047 h-1. Despite these extremely slow growth rates, the proportion of viable cells remained high, and de novo synthesis and secretion of the VHH were observed. The average qP at the end of the retentostat was estimated at 0.019 mg g-1 h-1. Transcriptomics indicated that genes involved in protein biosynthesis were only moderately downregulated towards zero growth, while secretory pathway genes were mostly regulated in a manner seemingly detrimental to protein secretion. Adaptation to near-zero growth conditions of recombinant K. phaffii resulted in significant changes in the total protein, RNA, DNA and lipid content, and lipidomics revealed a complex adaptation pattern regarding the lipid class composition. The higher abundance of storage lipids as well as storage carbohydrates indicates that the cells are preparing for long-term survival.

CONCLUSIONS

In conclusion, retentostat cultivation proved to be a valuable tool to identify potential engineering targets to decouple growth and protein production and gain important insights into the physiological adaptation of K. phaffii to near-zero growth conditions.

A Machine Learning Approach for Predicting Essentiality of Metabolic Genes

The identification of essential genes is a key challenge in systems and synthetic biology, particularly for engineering metabolic pathways that convert feedstocks into valuable products. Assessment of gene essentiality at a genome scale requires large and costly growth assays of knockout strains. Here we describe a strategy to predict the essentiality of metabolic genes using binary classification algorithms. The approach combines elements from genome-scale metabolic models, directed graphs, and machine learning into a predictive model that can be trained on small knockout data. We demonstrate the efficacy of this approach using the most complete metabolic model of Escherichia coli and various machine learning algorithms for binary classification.

Structural and Biochemical Studies on Product Inhibition of S-Adenosylmethionine Synthetase from Corynebacterium glutamicum

S-Adenosylmethionine (SAM) acts as a methyl donor in living organisms, and S-adenosylmethionine synthetase (MetK) is an essential enzyme for cells, as it synthesizes SAM from methionine and adenosine triphosphate (ATP). This study determined the crystal structures of the apo form and adenosine/triphosphate complex form of MetK from Corynebacterium glutamicum (CgMetK). Results showed that CgMetK has an allosteric inhibitor binding site for the SAM product in the vicinity of the active site and is inhibited by SAM both competitively and noncompetitively. Through structure-guided protein engineering, the CgMetKE68A variant was developed that exhibited an almost complete release of inhibition by SAM with rather enhanced enzyme activity. The crystal structure of the CgMetKE68A variant revealed that the formation of a new hydrogen bond between Tyr66 and Glu102 by the E68A mutation disrupted the allosteric SAM binding site and also improved the protein thermal stability by strengthening the tetramerization of the enzyme.

Estimation of metabolic fluxes distribution in Saccharomyces cerevisiae during the production of volatile compounds of Tequila

A stoichiometric model for Saccharomyces cerevisiae is reconstructed to analyze the continuous fermentation process of agave juice in Tequila production. The metabolic model contains 94 metabolites and 117 biochemical reactions. From the above set of reactions, 93 of them are linked to internal biochemical reactions and 24 are related to transport fluxes between the medium and the cell. The central metabolism of S. cerevisiae includes the synthesis for 20 amino-acids, carbohydrates, lipids, DNA and RNA. Using flux balance analysis (FBA), different physiological states of S. cerevisiae are shown during the fermentative process; these states are compared with experimental data under different dilution rates (0.04-0.12 h$ ^{-1} $). Moreover, the model performs anabolic and catabolic biochemical reactions for the production of higher alcohols. The importance of the Saccharomyces cerevisiae genomic model in the area of alcoholic beverage fermentation is due to the fact that it allows to estimate the metabolic fluxes during the beverage fermentation process and a physiology state of the microorganism.

Synthesis and analysis of separation processes for extracellular chemicals generated from microbial conversions

Recent advances in metabolic engineering have enabled the production of chemicals via bio-conversion using microbes. However, downstream separation accounts for 60–80% of the total production cost in many cases. Previous work on microbial production of extracellular chemicals has been mainly restricted to microbiology, biochemistry, metabolomics, or techno-economic analysis for specific product examples such as succinic acid, xanthan gum, lycopene, etc. In these studies, microbial production and separation technologies were selected apriori without considering any competing alternatives. However, technology selection in downstream separation and purification processes can have a major impact on the overall costs, product recovery, and purity. To this end, we apply a superstructure optimization based framework that enables the identification of critical technologies and their associated parameters in the synthesis and analysis of separation processes for extracellular chemicals generated from microbial conversions. We divide extracellular chemicals into three categories based on their physical properties, such as water solubility, physical state, relative density, volatility, etc. We analyze three major extracellular product categories (insoluble light, insoluble heavy and soluble) in detail and provide suggestions for additional product categories through extension of our analysis framework. The proposed analysis and results provide significant insights for technology selection and enable streamlined decision making when faced with any microbial product that is released extracellularly. The parameter variability analysis for the product as well as the associated technologies and comparison with novel alternatives is a key feature which forms the basis for designing better bioseparation strategies that have potential for commercial scalability and can compete with traditional chemical production methods.

Cell size and morphological properties of yeast Saccharomyces cerevisiae in relation to growth temperature

Cell volume is an important parameter for modelling cellular processes. Temperature-induced variability of cellular size, volume, intracellular granularity, a fraction of budding cells of yeast Saccharomyces cerevisiae CEN.PK 113-7D (in anaerobic glucose unlimited batch cultures) were measured by flow cytometry and matched with the performance of the biomass growth (maximal specific growth rate (μmax), specific rate of glucose consumption, the rate of maintenance, biomass yield on glucose). The critical diameter of single cells was 7.94 μm and it is invariant at growth temperatures above 18.5°C. Below 18.5°C, it exponentially increases up to 10.2 μm. The size of the bud linearly depends on μmax, and it is between 50% at 5°C and 90% at 31°C of the averaged single cell. The intracellular granularity (side scatter channel (SSC)-index) negatively depends on μmax. There are two temperature regions (5-31°C vs. 33-40°C) where the relationship between SSC-index and various cellular parameters differ significantly. In supraoptimal temperature range (33-40°C), cells are less granulated perhaps due to a higher rate of the maintenance. There is temperature dependent passage through the checkpoints in the cell cycle which influences the μmax. The results point to the existence of two different morphological states of yeasts in these different temperature regions.

Improved electrocompetence and metabolic engineering of Clostridium pasteurianum reveals a new regulation pattern of glycerol fermentation

Clostridium pasteurianum produces industrially valuable chemicals such as n-butanol and 1,3-propanediol from fermentations of glycerol and glucose. Metabolic engineering for increased yields of selective compounds is not well established in this microorganism. In order to study carbon fluxes and to selectively increase butanol yields, we integrated the latest advances in genome editing to obtain an electrocompetent Clostridium pasteurianum strain for further engineering. Deletion of the glycerol dehydratase large subunit (dhaB) using an adapted S. pyogenes Type II CRISPR/Cas9 nickase system resulted in a 1,3-propanediol-deficient mutant producing butanol as the main product. Surprisingly, the mutant was able to grow on glycerol as the sole carbon source. In spite of reduced growth, butanol yields were highly increased. Metabolic flux analysis revealed an important role of the newly identified electron bifurcation pathway for crotonyl-CoA to butyryl-CoA conversion in the regulation of redox balance. Compared to the parental strain, the electron bifurcation pathway flux of the dhaB mutant increased from 8 to 46% of the overall flux from crotonyl-CoA to butyryl-CoA and butanol, indicating a new, 1,3-propanediol-independent pattern of glycerol fermentation in Clostridium pasteurianum.

A Review of Dynamic Modeling Approaches and Their Application in Computational Strain Optimization for Metabolic Engineering

Mathematical modeling is a key process to describe the behavior of biological networks. One of the most difficult challenges is to build models that allow quantitative predictions of the cells' states along time. Recently, this issue started to be tackled through novel in silico approaches, such as the reconstruction of dynamic models, the use of phenotype prediction methods, and pathway design via efficient strain optimization algorithms. The use of dynamic models, which include detailed kinetic information of the biological systems, potentially increases the scope of the applications and the accuracy of the phenotype predictions. New efforts in metabolic engineering aim at bridging the gap between this approach and other different paradigms of mathematical modeling, as constraint-based approaches. These strategies take advantage of the best features of each method, and deal with the most remarkable limitation—the lack of available experimental information—which affects the accuracy and feasibility of solutions. Parameter estimation helps to solve this problem, but adding more computational cost to the overall process. Moreover, the existing approaches include limitations such as their scalability, flexibility, convergence time of the simulations, among others. The aim is to establish a trade-off between the size of the model and the level of accuracy of the solutions. In this work, we review the state of the art of dynamic modeling and related methods used for metabolic engineering applications, including approaches based on hybrid modeling. We describe approaches developed to undertake issues regarding the mathematical formulation and the underlying optimization algorithms, and that address the phenotype prediction by including available kinetic rate laws of metabolic processes. Then, we discuss how these have been used and combined as the basis to build computational strain optimization methods for metabolic engineering purposes, how they lead to bi-level schemes that can be used in the industry, including a consideration of their limitations.

Genetic and substrate-level modulation of Bacillus subtilis physiology for enhanced extracellular human interferon gamma production

Human interferon-gamma (hIFNG) production is limited by various gene-level bottlenecks including translation, protein folding, and secretion which depends upon the physiological state of the organism. In this study gene-level and substrate-level modulations have been used to control Bacillus subtilis physiology for >15 fold extracellular soluble hIFNG production. Two variants of the native human interferon-gamma gene (hifng) were designed and synthesized, namely, cohifng_his and cohifng having codon adaptation index 25.33 and 26.89% higher than the native gene, respectively. BScoIFNG and BScoIFNGhis with ΔG of -100.0 and -113.7 kcal mol^-1 resulted in 30 and 6.5% higher hIFNG compared to the native gene in complex medium. BScoIFNG produced 1.53 fold higher hIFNG using glucose-based defined medium as compared to the complex medium by modulating the physiological parameter growth rate from 0.35 to 0.26 hr^-1. Further modulatory effect of various phosphotransferase transport system (PTS) and no-PTS sugars, sugar alcohols, and organic acids was quantified on the physiology of B. subtilis WB800N for extracellular hIFNG production. Sorbitol and glycerol emerged as the best hIFNG producers with lowest growth and substrate consumption rates. BScoIFNG produced maximum 3.15 mg L^-1 hIFNG at 50 g L^-1 glycerol with highest hIFNG yield (Y_p/x = 0.136) and lowest substrate uptake rate (q_s = 0.26).

Estimation of time-varying growth, uptake and excretion rates from dynamic metabolomics data

Motivation

Technological advances in metabolomics have made it possible to monitor the concentration of extracellular metabolites over time. From these data, it is possible to compute the rates of uptake and excretion of the metabolites by a growing cell population, providing precious information on the functioning of intracellular metabolism. The computation of the rate of these exchange reactions, however, is difficult to achieve in practice for a number of reasons, notably noisy measurements, correlations between the concentration profiles of the different extracellular metabolites, and discontinuties in the profiles due to sudden changes in metabolic regime.

Results

We present a method for precisely estimating time-varying uptake and excretion rates from time-series measurements of extracellular metabolite concentrations, specifically addressing all of the above issues. The estimation problem is formulated in a regularized Bayesian framework and solved by a combination of extended Kalman filtering and smoothing. The method is shown to improve upon methods based on spline smoothing of the data. Moreover, when applied to two actual datasets, the method recovers known features of overflow metabolism in Escherichia coli and Lactococcus lactis, and provides evidence for acetate uptake by L. lactis after glucose exhaustion. The results raise interesting perspectives for further work on rate estimation from measurements of intracellular metabolites.

Availability and implementation

The Matlab code for the estimation method is available for download at https://team.inria.fr/ibis/rate-estimation-software/, together with the datasets.

Supplementary information

Supplementary data are available at Bioinformatics online.

Mathematical modelling of microbes: metabolism, gene expression and growth

The growth of microorganisms involves the conversion of nutrients in the environment into biomass, mostly proteins and other macromolecules. This conversion is accomplished by networks of biochemical reactions cutting across cellular functions, such as metabolism, gene expression, transport and signalling. Mathematical modelling is a powerful tool for gaining an understanding of the functioning of this large and complex system and the role played by individual constituents and mechanisms. This requires models of microbial growth that provide an integrated view of the reaction networks and bridge the scale from individual reactions to the growth of a population. In this review, we derive a general framework for the kinetic modelling of microbial growth from basic hypotheses about the underlying reaction systems. Moreover, we show that several families of approximate models presented in the literature, notably flux balance models and coarse-grained whole-cell models, can be derived with the help of additional simplifying hypotheses. This perspective clearly brings out how apparently quite different modelling approaches are related on a deeper level, and suggests directions for further research.

Integration of Genome Scale Metabolic Networks and Gene Regulation of Metabolic Enzymes With Physiologically Based Pharmacokinetics

The scope of physiologically based pharmacokinetic (PBPK) modeling can be expanded by assimilation of the mechanistic models of intracellular processes from systems biology field. The genome scale metabolic networks (GSMNs) represent a whole set of metabolic enzymes expressed in human tissues. Dynamic models of the gene regulation of key drug metabolism enzymes are available. Here, we introduce GSMNs and review ongoing work on integration of PBPK, GSMNs, and metabolic gene regulation. We demonstrate example models.

Regulation-Structured Dynamic Metabolic Model Provides a Potential Mechanism for Delayed Enzyme Response in Denitrification Process

In a recent study of denitrification dynamics in hyporheic zone sediments, we observed a significant time lag (up to several days) in enzymatic response to the changes in substrate concentration. To explore an underlying mechanism and understand the interactive dynamics between enzymes and nutrients, we developed a trait-based model that associates a community's traits with functional enzymes, instead of typically used species guilds (or functional guilds). This enzyme-based formulation allows to collectively describe biogeochemical functions of microbial communities without directly parameterizing the dynamics of species guilds, therefore being scalable to complex communities. As a key component of modeling, we accounted for microbial regulation occurring through transcriptional and translational processes, the dynamics of which was parameterized based on the temporal profiles of enzyme concentrations measured using a new signature peptide-based method. The simulation results using the resulting model showed several days of a time lag in enzymatic responses as observed in experiments. Further, the model showed that the delayed enzymatic reactions could be primarily controlled by transcriptional responses and that the dynamics of transcripts and enzymes are closely correlated. The developed model can serve as a useful tool for predicting biogeochemical processes in natural environments, either independently or through integration with hydrologic flow simulators.

Metabolic modeling to identify engineering targets for Komagataella phaffii: The effect of biomass composition on gene target identification

Genome‐scale metabolic models are valuable tools for the design of novel strains of industrial microorganisms, such as Komagataella phaffii (syn. Pichia pastoris). However, as is the case for many industrial microbes, there is no executable metabolic model for K. phaffiii that confirms to current standards by providing the metabolite and reactions IDs, to facilitate model extension and reuse, and gene‐reaction associations to enable identification of targets for genetic manipulation. In order to remedy this deficiency, we decided to reconstruct the genome‐scale metabolic model of K. phaffii by reconciling the extant models and performing extensive manual curation in order to construct an executable model (Kp.1.0) that conforms to current standards. We then used this model to study the effect of biomass composition on the predictive success of the model. Twelve different biomass compositions obtained from published empirical data obtained under a range of growth conditions were employed in this investigation. We found that the success of Kp1.0 in predicting both gene essentiality and growth characteristics was relatively unaffected by biomass composition. However, we found that biomass composition had a profound effect on the distribution of the fluxes involved in lipid, DNA, and steroid biosynthetic processes, cellular alcohol metabolic process, and oxidation‐reduction process. Furthermore, we investigated the effect of biomass composition on the identification of suitable target genes for strain development. The analyses revealed that around 40% of the predictions of the effect of gene overexpression or deletion changed depending on the representation of biomass composition in the model. Considering the robustness of the in silico flux distributions to the changing biomass representations enables better interpretation of experimental results, reduces the risk of wrong target identification, and so both speeds and improves the process of directed strain development.

Effect of resistant and digestible rice starches on human cytokine and lactate metabolic networks in serum

Resistant starch generated after treating ordinary starch is of great significance to human health in the countries with overnutrition. However, its functional evaluation in the human body has been rarely reported. By determining the lactate metabolic flux, 12 serum enzymes expression level and 38 serum cytokines in healthy volunteers, the variation in cytokine network and lactate metabolic network in serum were investigated to compare the mechanism of the physiological effects between the two starches. The results indicated that compared with digestible starch, resistant starch had anti-inflammatory effects, increased anabolism, and decreased catabolism. Further, the intercellular communication networks including cytokine and lactate metabolic networks were mapped out. The relationship suggested that resistant starch might affect and control the secretion of cytokines to regulate lactate metabolic network in the body, promoting the development of immunometabolism.

METABOLIC RECONSTRUCTION FOR DEVELOPING A TISSUE-SPECIFIC MODEL FOR CATTLE GLYCOLYSIS USING COBRA

This study aims at developing a tissue-specific model for glycolysis in bovine mammary gland epithelial cells by incorporating gene expression data into metabolic reactions. Two types of data sets were embedded in the COnstraint-Based Reconstruction Analysis (COBRA) toolbox: metabolic reactions that overlay bovine genetic information into human glycolysis data from a public database and gene expression data of cattle acquired from lab experiments. As a result, we successfully generated a tissue-specific model of bovine glycolysis in bovine mammary gland epithelial cells, providing information on expression of metabolic pathways and gene annotations still required for curation.

Rewriting yeast central carbon metabolism for industrial isoprenoid production

A bio-based economy has the potential to provide sustainable substitutes for petroleum-based products and new chemical building blocks for advanced materials. We previously engineered Saccharomyces cerevisiae for industrial production of the isoprenoid artemisinic acid for use in antimalarial treatments. Adapting these strains for biosynthesis of other isoprenoids such as β-farnesene (C₁₅H₂₄), a plant sesquiterpene with versatile industrial applications, is straightforward. However, S. cerevisiae uses a chemically inefficient pathway for isoprenoid biosynthesis, resulting in yield and productivity limitations incompatible with commodity-scale production. Here we use four non-native metabolic reactions to rewire central carbon metabolism in S. cerevisiae, enabling biosynthesis of cytosolic acetyl coenzyme A (acetyl-CoA, the two-carbon isoprenoid precursor) with a reduced ATP requirement, reduced loss of carbon to CO₂-emitting reactions, and improved pathway redox balance. We show that strains with rewired central metabolism can devote an identical quantity of sugar to farnesene production as control strains, yet produce 25% more farnesene with that sugar while requiring 75% less oxygen. These changes lower feedstock costs and dramatically increase productivity in industrial fermentations which are by necessity oxygen-constrained. Despite altering key regulatory nodes, engineered strains grow robustly under taxing industrial conditions, maintaining stable yield for two weeks in broth that reaches >15% farnesene by volume. This illustrates that rewiring yeast central metabolism is a viable strategy for cost-effective, large-scale production of acetyl-CoA-derived molecules.

Metabolic processes of Methanococcus maripaludis and potential applications

Methanococcus maripaludis is a rapidly growing, fully sequenced, genetically tractable model organism among hydrogenotrophic methanogens. It has the ability to convert CO2 and H2 into a useful cleaner energy fuel (CH4). In fact, this conversion enhances in the presence of free nitrogen as the sole nitrogen source due to prolonged cell growth. Given the global importance of GHG emissions and climate change, diazotrophy can be attractive for carbon capture and utilization applications from appropriately treated flue gases, where surplus hydrogen is available from renewable electricity sources. In addition, M. maripaludis can be engineered to produce other useful products such as terpenoids, hydrogen, methanol, etc. M. maripaludis with its unique abilities has the potential to be a workhorse like Escherichia coli and S. cerevisiae for fundamental and experimental biotechnology studies. More than 100 experimental studies have explored different specific aspects of the biochemistry and genetics of CO2 and N2 fixation by M. maripaludis. Its genome-scale metabolic model (iMM518) also exists to study genetic perturbations and complex biological interactions. However, a comprehensive review describing its cell structure, metabolic processes, and methanogenesis is still lacking in the literature. This review fills this crucial gap. Specifically, it integrates distributed information from the literature to provide a complete and detailed view for metabolic processes such as acetyl-CoA synthesis, pyruvate synthesis, glycolysis/gluconeogenesis, reductive tricarboxylic acid (RTCA) cycle, non-oxidative pentose phosphate pathway (NOPPP), nitrogen metabolism, amino acid metabolism, and nucleotide biosynthesis. It discusses energy production via methanogenesis and its relation to metabolism. Furthermore, it reviews taxonomy, cell structure, culture/storage conditions, molecular biology tools, genome-scale models, and potential industrial and environmental applications. Through the discussion, it develops new insights and hypotheses from experimental and modeling observations, and identifies opportunities for further research and applications.

Synthetic and systems biology for microbial production of commodity chemicals

The combination of synthetic and systems biology is a powerful framework to study fundamental questions in biology and produce chemicals of immediate practical application such as biofuels, polymers, or therapeutics. However, we cannot yet engineer biological systems as easily and precisely as we engineer physical systems. In this review, we describe the path from the choice of target molecule to scaling production up to commercial volumes. We present and explain some of the current challenges and gaps in our knowledge that must be overcome in order to bring our bioengineering capabilities to the level of other engineering disciplines. Challenges start at molecule selection, where a difficult balance between economic potential and biological feasibility must be struck. Pathway design and construction have recently been revolutionized by next-generation sequencing and exponentially improving DNA synthesis capabilities. Although pathway optimization can be significantly aided by enzyme expression characterization through proteomics, choosing optimal relative protein expression levels for maximum production is still the subject of heuristic, non-systematic approaches. Toxic metabolic intermediates and proteins can significantly affect production, and dynamic pathway regulation emerges as a powerful but yet immature tool to prevent it. Host engineering arises as a much needed complement to pathway engineering for high bioproduct yields; and systems biology approaches such as stoichiometric modeling or growth coupling strategies are required. A final, and often underestimated, challenge is the successful scale up of processes to commercial volumes. Sustained efforts in improving reproducibility and predictability are needed for further development of bioengineering.

Chemical characterization of fatty acids, alkanes, n-diols and alkyl esters produced by a mixed culture of Trichoderma koningii and Penicillium janthinellum grown aerobically on undecanoic acid, potatoe dextrose and their mixture

Little is known about the mixed fungal synthesis of high-value aliphatics derived from the metabolism of simple and complex carbon substrates. Trichoderma koningii and Penicillium janthinellum were fed with undecanoic acid (UDA), potatoe dextrose broth (PDB), and their mixture. Pyrolysis Field Ionization Mass Spectrometry (Py-FIMS) together with (1)H and (13)C Nuclear Magnetic Resonance (NMR) characterized CHCl3 soluble aliphatics in the fungal cell culture. Data from NMR and Py-FIMS analysis were complementary to each other. On average, the mixed fungal species produced mostly fatty acids (28% of total ion intensity, TII) > alkanes (2% of TII) > n-diols (2% of TII) > and alkyl esters (0.8% of TII) when fed with UDA, PDB or UDA+PDB. The cell culture accumulated aliphatics extracellularly, although most of the identified compounds accumulated intracellularly. The mixed fungal culture produced high-value chemicals from the metabolic conversion of simple and complex carbon substrates.

Kinetics of Reactive Modules Adds Discriminative Dimensions for Selective Cell Imaging

Living cells are chemical mixtures of exceptional interest and significance, whose investigation requires the development of powerful analytical tools fulfilling the demanding constraints resulting from their singular features. In particular, multiplexed observation of a large number of molecular targets with high spatiotemporal resolution appears highly desirable. One attractive road to address this analytical challenge relies on engaging the targets in reactions and exploiting the rich kinetic signature of the resulting reactive module, which originates from its topology and its rate constants. This review explores the various facets of this promising strategy. We first emphasize the singularity of the content of a living cell as a chemical mixture and suggest that its multiplexed observation is significant and timely. Then, we show that exploiting the kinetics of analytical processes is relevant to selectively detect a given analyte: upon perturbing the system, the kinetic window associated to response read-out has to be matched with that of the targeted reactive module. Eventually, we introduce the state-of-the-art of cell imaging exploiting protocols based on reaction kinetics and draw some promising perspectives.

Theoretical approaches for the dynamics of complex biological systems from information of networks

Modern biology has provided many examples of large networks describing the interactions between multiple species of bio-molecules. It is believed that the dynamics of molecular activities based on such networks are the origin of biological functions. On the other hand, we have a limited understanding for dynamics of molecular activity based on networks. To overcome this problem, we have developed two structural theories, by which the important aspects of the dynamical properties of the system are determined only from information on the network structure, without assuming other quantitative details. The first theory, named Linkage Logic, determines a subset of molecules in regulatory networks, by which any long-term dynamical behavior of the whole system can be identified/controlled. The second theory, named Structural Sensitivity Analysis, determines the sensitivity responses of the steady state of chemical reaction networks to perturbations of the reaction rate. The first and second theories investigate the dynamical properties of regulatory and reaction networks, respectively. The first theory targets the attractors of the regulatory network systems, whereas the second theory applies only to the steady states of the reaction network systems, but predicts their detailed behavior. To demonstrate the utility of our methods several biological network systems, and show they are practically useful to analyze behaviors of biological systems.

A Method to Constrain Genome-Scale Models with 13C Labeling Data

Current limitations in quantitatively predicting biological behavior hinder our efforts to engineer biological systems to produce biofuels and other desired chemicals. Here, we present a new method for calculating metabolic fluxes, key targets in metabolic engineering, that incorporates data from ¹³C labeling experiments and genome-scale models. The data from ¹³C labeling experiments provide strong flux constraints that eliminate the need to assume an evolutionary optimization principle such as the growth rate optimization assumption used in Flux Balance Analysis (FBA). This effective constraining is achieved by making the simple but biologically relevant assumption that flux flows from core to peripheral metabolism and does not flow back. The new method is significantly more robust than FBA with respect to errors in genome-scale model reconstruction. Furthermore, it can provide a comprehensive picture of metabolite balancing and predictions for unmeasured extracellular fluxes as constrained by ¹³C labeling data. A comparison shows that the results of this new method are similar to those found through ¹³C Metabolic Flux Analysis (¹³C MFA) for central carbon metabolism but, additionally, it provides flux estimates for peripheral metabolism. The extra validation gained by matching 48 relative labeling measurements is used to identify where and why several existing COnstraint Based Reconstruction and Analysis (COBRA) flux prediction algorithms fail. We demonstrate how to use this knowledge to refine these methods and improve their predictive capabilities. This method provides a reliable base upon which to improve the design of biological systems.

While metabolic fluxes constitute the most direct window into a cell’s metabolism, their accurate measurement is non trivial. The gold standard for flux measurement involves providing a labeled feed where some of the carbon atoms have been substituted by isotopes with higher atomic mass (¹³C instead of ¹²C). The ensuing labeling found in intracellular metabolites is then used to computationally infer the metabolic fluxes that produced the observed pattern. However, this procedure is typically performed with small metabolic models encompassing only central carbon metabolism. The genomic revolution has afforded us easily available genomes and, with them, comprehensive genome-scale models of cellular metabolism. It would be desirable to use the ¹³C labeling experimental data to constrain genome-scale models: these data constrain fluxes very effectively and provide in the labeling data fit an obvious proof that the underlying model correctly explains measured quantities. Here, we introduce a rigorous, self-consistent method that uses the full amount of information contained in ¹³C labeling data to constrain fluxes for a genome-scale model where underlying assumptions are explicitly stated.

Flux balance analysis in the production of clavulanic acid by Streptomyces clavuligerus

In this work, in silico flux balance analysis is used for predicting the metabolic behavior of Streptomyces clavuligerus during clavulanic acid production. To choose the best objective function for use in the analysis, three different optimization problems are evaluated inside the flux balance analysis formulation: (i) maximization of the specific growth rate, (ii) maximization of the ATP yield, and (iii) maximization of clavulanic acid production. Maximization of ATP yield showed the best predictions for the cellular behavior. Therefore, flux balance analysis using ATP as objective function was used for analyzing different scenarios of nutrient limitations toward establishing the effect of limiting the carbon, nitrogen, phosphorous, and oxygen sources on the growth and clavulanic acid production rates. Obtained results showed that ammonia and phosphate limitations are the ones most strongly affecting clavulanic acid biosynthesis. Furthermore, it was possible to identify the ornithine flux from the urea cycle and the α-ketoglutarate flux from the TCA cycle as the most determinant internal fluxes for promoting clavulanic acid production.

Salmonella typhimurium and Escherichia coli dissimilarity: Closely related bacteria with distinct metabolic profiles

Live attenuated strains of Salmonella typhimurium have been extensively investigated as vaccines for a number of infectious diseases. However, there is still little information available concerning aspects of their metabolism. S. typhimurium and Escherichia coli show a high degree of similarity in terms of their genome contents and metabolic networks. However, this work presents experimental evidence showing that significant differences exist in their abilities to direct carbon fluxes to biomass and energy production. It is important to study the metabolism of Salmonella to elucidate the formation of acetate and other metabolites involved in optimizing the production of biomass, essential for the development of recombinant vaccines. The metabolism of Salmonella under aerobic conditions was assessed using continuous cultures performed at dilution rates ranging from 0.1 to 0.67 h(-1), with glucose as main substrate. Acetate assimilation and glucose metabolism under anaerobic conditions were also investigated using batch cultures. Chemostat cultivations showed deviation of carbon towards acetate formation, starting at dilution rates above 0.1 h(-1). This differed from previous findings for E. coli, where acetate accumulation was only detected at dilution rates exceeding 0.4 h(-1), and was due to the lower rate of acetate assimilation by S. typhimurium under aerobic conditions. Under anaerobic conditions, both microorganisms mainly produced ethanol, acetate, and formate. A genome-scale metabolic model, reconstructed for Salmonella based on an E. coli model, provided a poor description of the mixed fermentation pattern observed during Salmonella cultures, reinforcing the different patterns of carbon utilization exhibited by these closely related bacteria.

A critical review on properties and applications of microbial l-asparaginases

l-Asparaginase is one of the main drugs used in the treatment of acute lymphoblastic leukemia (ALL), a commonly diagnosed pediatric cancer. Although several microorganisms are found to produce l-asparaginase, only the purified enzymes from E. coli and Erwinia chrysanthemi are employed in the clinical and therapeutic applications in humans. However, their therapeutic response seldom occurs without some evidence of hypersensitivity and other toxic side effects. l-Asparaginase is also of prospective use in food industry to reduce the formation of acrylamide in fried, roasted or baked food products. This review is an attempt to compile information on the properties of l-asparaginases obtained from different microorganisms. The complications involved with the therapeutic use of the currently available l-asparaginases, and the enzyme's potential application as a food processing aid to mitigate acrylamide formation have also been reviewed. Further, avenues for searching alternate sources of l-asparaginase have been discussed, highlighting the prospects of endophytic microorganisms as a possible source of l-asparaginases with varied biochemical and pharmacological properties.

Effect of pH and dilution rate on specific production rate of extra cellular metabolites by Lactobacillus salivarius UCO_979C in continuous culture

The effect of pH and dilution rate on the production of extracellular metabolites of Lactobacillus salivarius UCO_979 was studied. The experiments were carried out in continuous mode, with chemically defined culture medium at a temperature of 37 °C, 200 rpm agitation and synthetic air flow of 100 ml/min. Ethanol, acetic acid, formic acid, lactic acid and glucose were quantified through HPLC, while exopolysaccharide (EPS) was extracted with ethanol and quantified through the Dubois method. The results showed no linear trends for the specific production of lactic acid, EPS, acetic acid and ethanol, while the specific glucose consumption and ATP production rates showed linear trends. There was a metabolic change of the strain for dilution rates below 0.3 h(-1). The pH had a significant effect on the metabolism of the strain, which was evidenced by a higher specific glucose consumption and increased production of ATP at pH 6 compared with that obtained at pH 7. This work shows not only the metabolic capabilities of L. salivarius UCO_979C, but also shows that it is possible to quantify some molecules associated with its current use as gastrointestinal probiotic, especially regarding the production of organic acids and EPS.

An integrated network visualization framework towards metabolic engineering applications

Background

Over the last years, several methods for the phenotype simulation of microorganisms, under specified genetic and environmental conditions have been proposed, in the context of Metabolic Engineering (ME). These methods provided insight on the functioning of microbial metabolism and played a key role in the design of genetic modifications that can lead to strains of industrial interest. On the other hand, in the context of Systems Biology research, biological network visualization has reinforced its role as a core tool in understanding biological processes. However, it has been scarcely used to foster ME related methods, in spite of the acknowledged potential.

Results

In this work, an open-source software that aims to fill the gap between ME and metabolic network visualization is proposed, in the form of a plugin to the OptFlux ME platform. The framework is based on an abstract layer, where the network is represented as a bipartite graph containing minimal information about the underlying entities and their desired relative placement. The framework provides input/output support for networks specified in standard formats, such as XGMML, SBGN or SBML, providing a connection to genome-scale metabolic models. An user-interface makes it possible to edit, manipulate and query nodes in the network, providing tools to visualize diverse effects, including visual filters and aspect changing (e.g. colors, shapes and sizes). These tools are particularly interesting for ME, since they allow overlaying phenotype simulation results or elementary flux modes over the networks.

Conclusions

The framework and its source code are freely available, together with documentation and other resources, being illustrated with well documented case studies.

Electronic supplementary material

The online version of this article (doi:10.1186/s12859-014-0420-0) contains supplementary material, which is available to authorized users.

Pseudomonas 2.0: genetic upgrading of P. putida KT2440 as an enhanced host for heterologous gene expression

Background

Because of its adaptability to sites polluted with toxic chemicals, the model soil bacterium Pseudomonas putida is naturally endowed with a number of metabolic and stress-endurance qualities which have considerable value for hosting energy-demanding and redox reactions thereof. The growing body of knowledge on P. putida strain KT2440 has been exploited for the rational design of a derivative strain in which the genome has been heavily edited in order to construct a robust microbial cell factory.

Results

Eleven non-adjacent genomic deletions, which span 300 genes (i.e., 4.3% of the entire P. putida KT2440 genome), were eliminated; thereby enhancing desirable traits and eliminating attributes which are detrimental in an expression host. Since ATP and NAD(P)H availability – as well as genetic instability, are generally considered to be major bottlenecks for the performance of platform strains, a suite of functions that drain high-energy phosphate from the cells and/or consume NAD(P)H were targeted in particular, the whole flagellar machinery. Four prophages, two transposons, and three components of DNA restriction-modification systems were eliminated as well. The resulting strain (P. putida EM383) displayed growth properties (i.e., lag times, biomass yield, and specific growth rates) clearly superior to the precursor wild-type strain KT2440. Furthermore, it tolerated endogenous oxidative stress, acquired and replicated exogenous DNA, and survived better in stationary phase. The performance of a bi-cistronic GFP-LuxCDABE reporter system as a proxy of combined metabolic vitality, revealed that the deletions in P. putida strain EM383 brought about an increase of >50% in the overall physiological vigour.

Conclusion

The rationally modified P. putida strain allowed for the better functional expression of implanted genes by directly improving the metabolic currency that sustains the gene expression flow, instead of resorting to the classical genetic approaches (e.g., increasing the promoter strength in the DNA constructs of interest).

Electronic supplementary material

The online version of this article (doi:10.1186/s12934-014-0159-3) contains supplementary material, which is available to authorized users.

Quantifying biomass composition by gas chromatography/mass spectrometry

We developed a set of methods for the quantification of four major components of microbial biomass using gas chromatography/mass spectrometry (GC/MS). Specifically, methods are described to quantify amino acids, RNA, fatty acids, and glycogen, which comprise an estimated 88% of the dry weight of Escherichia coli. Quantification is performed by isotope ratio analysis with fully (13)C-labeled biomass as internal standard, which is generated by growing E. coli on [U-(13)C]glucose. This convenient, reliable, and accurate single-platform (GC/MS) workflow for measuring biomass composition offers significant advantages over existing methods. We demonstrate the consistency, accuracy, precision, and utility of this procedure by applying it to three metabolically unique E. coli strains. The presented methods will have widespread applicability in systems microbiology and bioengineering.

Method for finding metabolic properties based on the general growth law. Liver examples. A general framework for biological modeling

We propose a method for finding metabolic parameters of cells, organs and whole organisms, which is based on the earlier discovered general growth law. Based on the obtained results and analysis of available biological models, we propose a general framework for modeling biological phenomena and discuss how it can be used in Virtual Liver Network project. The foundational idea of the study is that growth of cells, organs, systems and whole organisms, besides biomolecular machinery, is influenced by biophysical mechanisms acting at different scale levels. In particular, the general growth law uniquely defines distribution of nutritional resources between maintenance needs and biomass synthesis at each phase of growth and at each scale level. We exemplify the approach considering metabolic properties of growing human and dog livers and liver transplants. A procedure for verification of obtained results has been introduced too. We found that two examined dogs have high metabolic rates consuming about 0.62 and 1 gram of nutrients per cubic centimeter of liver per day, and verified this using the proposed verification procedure. We also evaluated consumption rate of nutrients in human livers, determining it to be about 0.088 gram of nutrients per cubic centimeter of liver per day for males, and about 0.098 for females. This noticeable difference can be explained by evolutionary development, which required females to have greater liver processing capacity to support pregnancy. We also found how much nutrients go to biomass synthesis and maintenance at each phase of liver and liver transplant growth. Obtained results demonstrate that the proposed approach can be used for finding metabolic characteristics of cells, organs, and whole organisms, which can further serve as important inputs and constraints for many applications in biology (such as protein expression), biotechnology (synthesis of substances), and medicine.

Analyzing clonal variation of monoclonal antibody-producing CHO cell lines using an in silico metabolomic platform

Monoclonal antibody producing Chinese hamster ovary (CHO) cells have been shown to undergo metabolic changes when engineered to produce high titers of recombinant proteins. In this work, we have studied the distinct metabolism of CHO cell clones harboring an efficient inducible expression system, based on the cumate gene switch, and displaying different expression levels, high and low productivities, compared to that of the parental cells from which they were derived. A kinetic model for CHO cell metabolism was further developed to include metabolic regulation. Model calibration was performed using intracellular and extracellular metabolite profiles obtained from shake flask batch cultures. Model simulations of intracellular fluxes and ratios known as biomarkers revealed significant changes correlated with clonal variation but not to the recombinant protein expression level. Metabolic flux distribution mostly differs in the reactions involving pyruvate metabolism, with an increased net flux of pyruvate into the tricarboxylic acid (TCA) cycle in the high-producer clone, either being induced or non-induced with cumate. More specifically, CHO cell metabolism in this clone was characterized by an efficient utilization of glucose and a high pyruvate dehydrogenase flux. Moreover, the high-producer clone shows a high rate of anaplerosis from pyruvate to oxaloacetate, through pyruvate carboxylase and from glutamate to α-ketoglutarate, through glutamate dehydrogenase, and a reduced rate of cataplerosis from malate to pyruvate, through malic enzyme. Indeed, the increase of flux through pyruvate carboxylase was not driven by an increased anabolic demand. It is in fact linked to an increase of the TCA cycle global flux, which allows better regulation of higher redox and more efficient metabolic states. To the best of our knowledge, this is the first time a dynamic in silico platform is proposed to analyze and compare the metabolomic behavior of different CHO clones.

Improved understanding of gene expression regulation using systems biology

This article reviews the current state of systems biology approaches, including the experimental tools used to generate 'omic' data and computational frameworks to interpret this data. Through illustrative examples, systems biology approaches to understand gene expression and gene expression regulation are discussed. Some of the challenges facing this field and the future opportunities in the systems biology era are highlighted.

Fast reconstruction of compact context-specific metabolic network models

Systemic approaches to the study of a biological cell or tissue rely increasingly on the use of context-specific metabolic network models. The reconstruction of such a model from high-throughput data can routinely involve large numbers of tests under different conditions and extensive parameter tuning, which calls for fast algorithms. We present fastcore, a generic algorithm for reconstructing context-specific metabolic network models from global genome-wide metabolic network models such as Recon X. fastcore takes as input a core set of reactions that are known to be active in the context of interest (e.g., cell or tissue), and it searches for a flux consistent subnetwork of the global network that contains all reactions from the core set and a minimal set of additional reactions. Our key observation is that a minimal consistent reconstruction can be defined via a set of sparse modes of the global network, and fastcore iteratively computes such a set via a series of linear programs. Experiments on liver data demonstrate speedups of several orders of magnitude, and significantly more compact reconstructions, over a rival method. Given its simplicity and its excellent performance, fastcore can form the backbone of many future metabolic network reconstruction algorithms.

Impact of sustaining a controlled residual growth on polyhydroxybutyrate yield and production kinetics in Cupriavidus necator

In this study a complementary modeling and experimental approach was used to explore how growth controls the NADPH generation and availability, and the resulting impact on PHB (polyhydroxybutyrate) yields and kinetics. The results show that the anabolic demand allowed the NADPH production through the Entner-Doudoroff (ED) pathway, leading to a high maximal theoretical PHB production yield of 0.89 C mole C mole(-1); whereas without biomass production, NADPH regeneration is only possible via the isocitrate dehydrogenase leading to a theoretical yield of 0.67 C mole C mole(-1). Furthermore, the maximum specific rate of NADPH produced at maximal growth rate (to fulfil biomass requirement) was found to be the maximum set in every conditions, which by consequence determines the maximal PHB production rate. These results imply that sustaining a controlled residual growth improves the PHB specific production rate without altering production yield.