Metabolic control analysis: a survey of its theoretical and experimental development

Metabolic control analysis enabled the improvement of the L-cysteine production process with Escherichia coli

L-cysteine is an amino acid with relevance to the pharmaceutical, food, feed, and cosmetic industry. The environmental and societal impact of its chemical production has led to the development of more sustainable fermentative L-cysteine production processes with engineered E. coli based on glucose and thiosulfate as sulphur source. Still, most of the published processes show low yields. For the identification of further metabolic engineering targets, engineered E. coli cells were withdrawn from a fed-batch production process, followed by in vivo metabolic control analysis (MCA) based on the data of short-term perturbation experiments, metabolomics (LC-MS), and thermodynamic flux analysis (TFA). In vivo MCA indicated that the activities of the L-cysteine synthases of the cells withdrawn from the production process might be limiting, and we hypothesised that the L-cysteine precursor O-acetylserine (OAS) might be exported from the cells faster than it took to transform OAS into L-cysteine. By increasing the expression of the L-cysteine synthases, either sulfocysteine synthase or L-cysteine synthase, which transform OAS into L-cysteine, an improvement of up to 70% in specific L-cysteine productivity and up to 47% in the final L-cysteine concentration was achieved in standardised fed-batch processes thereby increasing the yield on glucose by more than 85 to 9.2% (w/w). KEY POINTS: • Metabolic control analysis was applied to analyse L-cysteine production with E. coli • OAS export was faster than its transformation to L-cysteine • Overexpression of L-cysteine synthases improved L-cysteine productivity and yield.

Integrating bacterial molecular genetics with chemical biology for renewed antibacterial drug discovery

The application of dyes to understanding the aetiology of infection inspired antimicrobial chemotherapy and the first wave of antibacterial drugs. The second wave of antibacterial drug discovery was driven by rapid discovery of natural products, now making up 69% of current antibacterial drugs. But now with the most prevalent natural products already discovered, ∼107 new soil-dwelling bacterial species must be screened to discover one new class of natural product. Therefore, instead of a third wave of antibacterial drug discovery, there is now a discovery bottleneck. Unlike natural products which are curated by billions of years of microbial antagonism, the vast synthetic chemical space still requires artificial curation through the therapeutics science of antibacterial drugs - a systematic understanding of how small molecules interact with bacterial physiology, effect desired phenotypes, and benefit the host. Bacterial molecular genetics can elucidate pathogen biology relevant to therapeutics development, but it can also be applied directly to understanding mechanisms and liabilities of new chemical agents with new mechanisms of action. Therefore, the next phase of antibacterial drug discovery could be enabled by integrating chemical expertise with systematic dissection of bacterial infection biology. Facing the ambitious endeavour to find new molecules from nature or new-to-nature which cure bacterial infections, the capabilities furnished by modern chemical biology and molecular genetics can be applied to prospecting for chemical modulators of new targets which circumvent prevalent resistance mechanisms.

The paradox of fatty-acid β-oxidation in muscle insulin resistance: Metabolic control and muscle heterogeneity

The skeletal muscle is a metabolically heterogeneous tissue that plays a key role in maintaining whole-body glucose homeostasis. It is well known that muscle insulin resistance (IR) precedes the development of type 2 diabetes. There is a consensus that the accumulation of specific lipid species in the tissue can drive IR. However, the role of the mitochondrial fatty-acid β-oxidation in IR and, consequently, in the control of glucose uptake remains paradoxical: interventions that either inhibit or activate fatty-acid β-oxidation have been shown to prevent IR. We here discuss the current theories and evidence for the interplay between β-oxidation and glucose uptake in IR. To address the underlying intricacies, we (1) dive into the control of glucose uptake fluxes into muscle tissues using the framework of Metabolic Control Analysis, and (2) disentangle concepts of flux and catalytic capacities taking into account skeletal muscle heterogeneity. Finally, we speculate about hitherto unexplored mechanisms that could bring contrasting evidence together. Elucidating how β-oxidation is connected to muscle IR and the underlying role of muscle heterogeneity enhances disease understanding and paves the way for new treatments for type 2 diabetes.

Deoxyxylulose 5-Phosphate Synthase Does Not Play a Major Role in Regulating the Methylerythritol 4-Phosphate Pathway in Poplar

The plastidic 2-C-methylerythritol 4-phosphate (MEP) pathway supplies the precursors of a large variety of essential plant isoprenoids, but its regulation is still not well understood. Using metabolic control analysis (MCA), we examined the first enzyme of this pathway, 1-deoxyxylulose 5-phosphate synthase (DXS), in multiple grey poplar (Populus × canescens) lines modified in their DXS activity. Single leaves were dynamically labeled with 13CO2 in an illuminated, climate-controlled gas exchange cuvette coupled to a proton transfer reaction mass spectrometer, and the carbon flux through the MEP pathway was calculated. Carbon was rapidly assimilated into MEP pathway intermediates and labeled both the isoprene released and the IDP+DMADP pool by up to 90%. DXS activity was increased by 25% in lines overexpressing the DXS gene and reduced by 50% in RNA interference lines, while the carbon flux in the MEP pathway was 25-35% greater in overexpressing lines and unchanged in RNA interference lines. Isoprene emission was also not altered in these different genetic backgrounds. By correlating absolute flux to DXS activity under different conditions of light and temperature, the flux control coefficient was found to be low. Among isoprenoid end products, isoprene itself was unchanged in DXS transgenic lines, but the levels of the chlorophylls and most carotenoids measured were 20-30% less in RNA interference lines than in overexpression lines. Our data thus demonstrate that DXS in the isoprene-emitting grey poplar plays only a minor part in controlling flux through the MEP pathway.

Erythrocyte metabolism

Our aim is to present an updated overview of the erythrocyte metabolism highlighting its richness and complexity. We have manually collected and connected the available biochemical pathways and integrated them into a functional metabolic map. The focus of this map is on the main biochemical pathways consisting of glycolysis, the pentose phosphate pathway, redox metabolism, oxygen metabolism, purine/nucleoside metabolism, and membrane transport. Other recently emerging pathways are also curated, like the methionine salvage pathway, the glyoxalase system, carnitine metabolism, and the lands cycle, as well as remnants of the carboxylic acid metabolism. An additional goal of this review is to present the dynamics of erythrocyte metabolism, providing key numbers used to perform basic quantitative analyses. By synthesizing experimental and computational data, we conclude that glycolysis, pentose phosphate pathway, and redox metabolism are the foundations of erythrocyte metabolism. Additionally, the erythrocyte can sense oxygen levels and oxidative stress adjusting its mechanics, metabolism, and function. In conclusion, fine-tuning of erythrocyte metabolism controls one of the most important biological processes, that is, oxygen loading, transport, and delivery.

Engineering Saccharomyces cerevisiae for fast vitamin-independent aerobic growth

Chemically defined media for cultivation of Saccharomyces cerevisiae strains are commonly supplemented with a mixture of multiple Class-B vitamins, whose omission leads to strongly reduced growth rates. Fast growth without vitamin supplementation is interesting for industrial applications, as it reduces costs and complexity of medium preparation and may decrease susceptibility to contamination by auxotrophic microbes. In this study, suboptimal growth rates of S. cerevisiae CEN.PK113-7D in the absence of pantothenic acid, para-aminobenzoic acid (pABA), pyridoxine, inositol and/or biotin were corrected by single or combined overexpression of ScFMS1, ScABZ1/ScABZ2, ScSNZ1/ScSNO1, ScINO1 and Cyberlindnera fabianii BIO1, respectively. Several strategies were explored to improve growth of S. cerevisiae CEN.PK113-7D in thiamine-free medium. Overexpression of ScTHI4 and/or ScTHI5 enabled thiamine-independent growth at 83% of the maximum specific growth rate of the reference strain in vitamin-supplemented medium. Combined overexpression of seven native S. cerevisiae genes and CfBIO1 enabled a maximum specific growth rate of 0.33 ± 0.01 h-1 in vitamin-free synthetic medium. This growth rate was only 17 % lower than that of a congenic reference strain in vitamin-supplemented medium. Physiological parameters of the engineered vitamin-independent strain in aerobic glucose-limited chemostat cultures (dilution rate 0.10 h-1) grown on vitamin-free synthetic medium were similar to those of similar cultures of the parental strain grown on vitamin-supplemented medium. Transcriptome analysis revealed only few differences in gene expression between these cultures, which primarily involved genes with roles in Class-B vitamin metabolism. These results pave the way for development of fast-growing vitamin-independent industrial strains of S. cerevisiae.

The metabolic control theory: Its development and its application to mitochondrial oxidative phosphorylation

Metabolic Control Theory (MCT) and Metabolic Control Analysis (MCA) are the two sides, theoretical and experimental, of the measurement of the sensitivity of metabolic networks in the vicinity of a steady state. We will describe the birth and the development of this theory from the first analyses of linear pathways up to a global mathematical theory applicable to any metabolic network. We will describe how the theory, given the global nature of mitochondrial oxidative phosphorylation, solved the problem of what controls mitochondrial ATP synthesis and then how it led to a better understanding of the differential tissue expression of human mitochondrial pathologies and of the heteroplasmy of mitochondrial DNA, leading to the concept of the threshold effect.

Integrative teaching of metabolic modeling and flux analysis with interactive python modules

The modeling of rates of biochemical reactions-fluxes-in metabolic networks is widely used for both basic biological research and biotechnological applications. A number of different modeling methods have been developed to estimate and predict fluxes, including kinetic and constraint-based (Metabolic Flux Analysis and flux balance analysis) approaches. Although different resources exist for teaching these methods individually, to-date no resources have been developed to teach these approaches in an integrative way that equips learners with an understanding of each modeling paradigm, how they relate to one another, and the information that can be gleaned from each. We have developed a series of modeling simulations in Python to teach kinetic modeling, metabolic control analysis, 13C-metabolic flux analysis, and flux balance analysis. These simulations are presented in a series of interactive notebooks with guided lesson plans and associated lecture notes. Learners assimilate key principles using models of simple metabolic networks by running simulations, generating and using data, and making and validating predictions about the effects of modifying model parameters. We used these simulations as the hands-on computer laboratory component of a four-day metabolic modeling workshop and participant survey results showed improvements in learners' self-assessed competence and confidence in understanding and applying metabolic modeling techniques after having attended the workshop. The resources provided can be incorporated in their entirety or individually into courses and workshops on bioengineering and metabolic modeling at the undergraduate, graduate, or postgraduate level.

Control analysis of collagen synthesis by glycine, proline and lysine in bovine chondrocytes in vitro - Its relevance for medicine and nutrition

Collagen synthesis is severely diminished in osteoarthritis; thus, enhancing it may help the regeneration of cartilage. Collagen synthesis is submitted to a large procollagen cycle where the greater part of the newly synthesized protein is degraded inside the cell producing a huge waste of material and energy. We have applied the Metabolic Control Analysis approach to study the control of collagen synthesis flux by means of the response coefficients of the flux with respect to glycine, proline and lysine. Our results show that the main cause of the procollagen cycle is a protein misfolding mainly due to glycine scarcity, as well as a moderate deficiency of proline and lysine for collagen synthesis. Thus, increasing these amino acids in the diet (especially glycine) may well be a strategy for helping cartilage regeneration by enhancing collagen synthesis and reducing its huge waste in the procollagen cycle; this possibly contributes to the treatment and prevention of osteoarthritis.

Personalised modelling of clinical heterogeneity between medium-chain acyl-CoA dehydrogenase patients

BACKGROUND

Monogenetic inborn errors of metabolism cause a wide phenotypic heterogeneity that may even differ between family members carrying the same genetic variant. Computational modelling of metabolic networks may identify putative sources of this inter-patient heterogeneity. Here, we mainly focus on medium-chain acyl-CoA dehydrogenase deficiency (MCADD), the most common inborn error of the mitochondrial fatty acid oxidation (mFAO). It is an enigma why some MCADD patients-if untreated-are at risk to develop severe metabolic decompensations, whereas others remain asymptomatic throughout life. We hypothesised that an ability to maintain an increased free mitochondrial CoA (CoASH) and pathway flux might distinguish asymptomatic from symptomatic patients.

RESULTS

We built and experimentally validated, for the first time, a kinetic model of the human liver mFAO. Metabolites were partitioned according to their water solubility between the bulk aqueous matrix and the inner membrane. Enzymes are also either membrane-bound or in the matrix. This metabolite partitioning is a novel model attribute and improved predictions. MCADD substantially reduced pathway flux and CoASH, the latter due to the sequestration of CoA as medium-chain acyl-CoA esters. Analysis of urine from MCADD patients obtained during a metabolic decompensation showed an accumulation of medium- and short-chain acylcarnitines, just like the acyl-CoA pool in the MCADD model. The model suggested some rescues that increased flux and CoASH, notably increasing short-chain acyl-CoA dehydrogenase (SCAD) levels. Proteome analysis of MCADD patient-derived fibroblasts indeed revealed elevated levels of SCAD in a patient with a clinically asymptomatic state. This is a rescue for MCADD that has not been explored before. Personalised models based on these proteomics data confirmed an increased pathway flux and CoASH in the model of an asymptomatic patient compared to those of symptomatic MCADD patients.

CONCLUSIONS

We present a detailed, validated kinetic model of mFAO in human liver, with solubility-dependent metabolite partitioning. Personalised modelling of individual patients provides a novel explanation for phenotypic heterogeneity among MCADD patients. Further development of personalised metabolic models is a promising direction to improve individualised risk assessment, management and monitoring for inborn errors of metabolism.

Sensitivity analysis of biochemical systems using bond graphs

The sensitivity of systems biology models to parameter variation can give insights into which parameters are most important for physiological function, and also direct efforts to estimate parameters. However, in general, kinetic models of biochemical systems do not remain thermodynamically consistent after perturbing parameters. To address this issue, we analyse the sensitivity of biological reaction networks in the context of a bond graph representation. We find that the parameter sensitivities can themselves be represented as bond graph components, mirroring potential mechanisms for controlling biochemistry. In particular, a sensitivity system is derived which re-expresses parameter variation as additional system inputs. The sensitivity system is then linearized with respect to these new inputs to derive a linear system which can be used to give local sensitivity to parameters in terms of linear system properties such as gain and time constant. This linear system can also be used to find so-called sloppy parameters in biological models. We verify our approach using a model of the Pentose Phosphate Pathway, confirming the reactions and metabolites most essential to maintaining the function of the pathway.

Metabolic Control Analysis of triacylglycerol accumulation in oilseed rape

The increasing global demand for vegetable oils will only be met if there are significant improvements in the productivity of the major oil crops, such as oilseed rape. Metabolic engineering offers the prospect of further gains in yield beyond that already achieved by breeding and selection but requires guidance as to the changes that need to be made. Metabolic Control Analysis, through measurement and estimation of flux control coefficients, can indicate which enzymes have the most influence on a desired flux. Some experiments have previously reported flux control coefficients for oil accumulation in the seeds of oilseed rape, and others have measured control coefficient distributions for multi-enzyme segments of oil synthesis in seed embryo metabolism measured in vitro. In addition, other reported manipulations of oil accumulation contain results that are exploited further here to calculate previously unknown flux control coefficients. These results are then assembled within a framework that allows an integrated interpretation of the controls on oil accumulation from the assimilation of CO₂ to deposition of oil in the seed. The analysis shows that the control is distributed to an extent that the gains from amplifying any single target are necessarily limited, but there are candidates for joint amplification that are likely to act synergistically to produce much more significant gains.

Metabolic imaging with deuterium labeled substrates

Deuterium metabolic imaging (DMI) is an emerging clinically-applicable technique for the non-invasive investigation of tissue metabolism. The generally short T1 values of 2H-labeled metabolites in vivo can compensate for the relatively low sensitivity of detection by allowing rapid signal acquisition in the absence of significant signal saturation. Studies with deuterated substrates, including [6,6'-2H2]glucose, [2H3]acetate, [2H9]choline and [2,3-2H2]fumarate have demonstrated the considerable potential of DMI for imaging tissue metabolism and cell death in vivo. The technique is evaluated here in comparison with established metabolic imaging techniques, including PET measurements of 2-deoxy-2-[18F]fluoro-d-glucose (FDG) uptake and 13C MR imaging of the metabolism of hyperpolarized 13C-labeled substrates.

Metabolic Engineering: Methodologies and Applications

Metabolic engineering aims to improve the production of economically valuable molecules through the genetic manipulation of microbial metabolism. While the discipline is a little over 30 years old, advancements in metabolic engineering have given way to industrial-level molecule production benefitting multiple industries such as chemical, agriculture, food, pharmaceutical, and energy industries. This review describes the design, build, test, and learn steps necessary for leading a successful metabolic engineering campaign. Moreover, we highlight major applications of metabolic engineering, including synthesizing chemicals and fuels, broadening substrate utilization, and improving host robustness with a focus on specific case studies. Finally, we conclude with a discussion on perspectives and future challenges related to metabolic engineering.

MIRTH: Metabolite Imputation via Rank-Transformation and Harmonization

Out of the thousands of metabolites in a given specimen, most metabolomics experiments measure only hundreds, with poor overlap across experimental platforms. Here, we describe Metabolite Imputation via Rank-Transformation and Harmonization (MIRTH), a method to impute unmeasured metabolite abundances by jointly modeling metabolite covariation across datasets which have heterogeneous coverage of metabolite features. MIRTH successfully recovers masked metabolite abundances both within single datasets and across multiple, independently-profiled datasets. MIRTH demonstrates that latent information about otherwise unmeasured metabolites is embedded within existing metabolomics data, and can be used to generate novel hypotheses and simplify existing metabolomic workflows.

Full humanization of the glycolytic pathway in Saccharomyces cerevisiae

Although transplantation of single genes in yeast plays a key role in elucidating gene functionality in metazoans, technical challenges hamper humanization of full pathways and processes. Empowered by advances in synthetic biology, this study demonstrates the feasibility and implementation of full humanization of glycolysis in yeast. Single gene and full pathway transplantation revealed the remarkable conservation of glycolytic and moonlighting functions and, combined with evolutionary strategies, brought to light context-dependent responses. Human hexokinase 1 and 2, but not 4, required mutations in their catalytic or allosteric sites for functionality in yeast, whereas hexokinase 3 was unable to complement its yeast ortholog. Comparison with human tissues cultures showed preservation of turnover numbers of human glycolytic enzymes in yeast and human cell cultures. This demonstration of transplantation of an entire essential pathway paves the way for establishment of species-, tissue-, and disease-specific metazoan models.

Dynamics and Sensitivity of Signaling Pathways

Purpose of Review

Signaling pathways serve to communicate information about extracellular conditions into the cell, to both the nucleus and cytoplasmic processes to control cell responses. Genetic mutations in signaling network components are frequently associated with cancer and can result in cells acquiring an ability to divide and grow uncontrollably. Because signaling pathways play such a significant role in cancer initiation and advancement, their constituent proteins are attractive therapeutic targets. In this review, we discuss how signaling pathway modeling can assist with identifying effective drugs for treating diseases, such as cancer. An achievement that would facilitate the use of such models is their ability to identify controlling biochemical parameters in signaling pathways, such as molecular abundances and chemical reaction rates, because this would help determine effective points of attack by therapeutics.

Recent Findings

We summarize the current state of understanding the sensitivity of phosphorylation cycles with and without sequestration. We also describe some basic properties of regulatory motifs including feedback and feedforward regulation.

Summary

Although much recent work has focused on understanding the dynamics and particularly the sensitivity of signaling networks in eukaryotic systems, there is still an urgent need to build more scalable models of signaling networks that can appropriately represent their complexity across different cell types and tumors.

A structural approach to understanding enzymatic regulation of chemical reaction networks

In living cells, chemical reactions are connected by sharing their products and substrates, and form complex systems, i.e. chemical reaction network. One of the largest missions in modern biology is to understand behaviors of such systems logically based on information of network structures. However, there are series of obstacles to study dynamical behaviors of complex network systems in biology. For example, network structure does not provide sufficient information to determine details of the dynamical behaviors. In this review, I will introduce a novel mathematical theory, structural sensitivity analysis, by which the responses of reaction systems upon the changes in enzyme activities/amounts are determined from network structure alone. The patterns of responses exhibit characteristic features, localization and hierarchy, depending on the topology of the network. The theory also shows that ranges of enzymatic regulations are governed by a mathematical law characterized by local topology of substructures. These findings imply that the network topology is one of the origins of biological robustness.

Non-linearity of Metabolic Pathways Critically Influences the Choice of Machine Learning Model

The use of machine learning (ML) in life sciences has gained wide interest over the past years, as it speeds up the development of high performing models. Important modeling tools in biology have proven their worth for pathway design, such as mechanistic models and metabolic networks, as they allow better understanding of mechanisms involved in the functioning of organisms. However, little has been done on the use of ML to model metabolic pathways, and the degree of non-linearity associated with them is not clear. Here, we report the construction of different metabolic pathways with several linear and non-linear ML models. Different types of data are used; they lead to the prediction of important biological data, such as pathway flux and final product concentration. A comparison reveals that the data features impact model performance and highlight the effectiveness of non-linear models (e.g., QRF: RMSE = 0.021 nmol·min-1 and R2 = 1 vs. Bayesian GLM: RMSE = 1.379 nmol·min-1 R2 = 0.823). It turns out that the greater the degree of non-linearity of the pathway, the better suited a non-linear model will be. Therefore, a decision-making support for pathway modeling is established. These findings generally support the hypothesis that non-linear aspects predominate within the metabolic pathways. This must be taken into account when devising possible applications of these pathways for the identification of biomarkers of diseases (e.g., infections, cancer, neurodegenerative diseases) or the optimization of industrial production processes.

Limited Mechanistic Link Between the Monod Equation and Methanogen Growth: a Perspective from Metabolic Modeling

The Monod equation has been widely applied as the general rate law of microbial growth, but its applications are not always successful. By drawing on the frameworks of kinetic and stoichiometric metabolic models and metabolic control analysis, the modeling reported here simulated the growth kinetics of a methanogenic microorganism and illustrated that different enzymes and metabolites control growth rate to various extents and that their controls peak at either very low, intermediate, or very high substrate concentrations. In comparison, with a single term and two parameters, the Monod equation only approximately accounts for the controls of rate-determining enzymes and metabolites at very high and very low substrate concentrations, but neglects the enzymes and metabolites whose controls are most notable at intermediate concentrations. These findings support a limited link between the Monod equation and methanogen growth, and unify the competing views regarding enzyme roles in shaping growth kinetics. The results also preclude a mechanistic derivation of the Monod equation from methanogen metabolic networks and highlight a fundamental challenge in microbiology: single-term expressions may not be sufficient for accurate prediction of microbial growth. IMPORTANCE The Monod equation has been widely applied to predict the rate of microbial growth, but its application is not always successful. Using a novel metabolic modeling approach, we simulated the growth of a methanogen and uncovered a limited mechanistic link between the Monod equation and the methanogen's metabolic network. Specifically, the equation provides an approximation to the controls by rate-determining metabolites and enzymes at very low and very high substrate concentrations, but it is missing the remaining enzymes and metabolites whose controls are most notable at intermediate concentrations. These results support the Monod equation as a useful approximation of growth rates and highlight a fundamental challenge in microbial kinetics: single-term rate expressions may not be sufficient for accurate prediction of microbial growth.

Multiple timescales in bacterial growth homeostasis

In balanced exponential growth, bacteria maintain many properties statistically stable for a long time: cell size, cell cycle time, and more. As these are strongly coupled variables, it is not a-priori obvious which are directly regulated and which are stabilized through interactions. Here, we address this problem by separating timescales in bacterial single-cell dynamics. Disentangling homeostatic set points from fluctuations around them reveals that some variables, such as growth-rate, cell size and cycle time, are "sloppy" with highly volatile set points. Quantifying the relative contribution of environmental and internal sources, we find that sloppiness is primarily driven by the environment. Other variables such as fold-change define "stiff" combinations of coupled variables with robust set points. These results are manifested geometrically as a control manifold in the space of variables: set points span a wide range of values within the manifold, whereas out-of-manifold deviations are constrained. Our work offers a generalizable data-driven approach for identifying control variables in a multidimensional system.

Dynamic Control Balancing Cell Proliferation and Inflammation is Crucial for an Effective Immune Response to Malaria

Malaria has a complex pathology with varying manifestations and symptoms, effects on host tissues, and different degrees of severity and ultimate outcome, depending on the causative Plasmodium pathogen and host species. Previously, we compared the peripheral blood transcriptomes of two macaque species (Macaca mulatta and Macaca fascicularis) in response to acute primary infection by Plasmodium knowlesi. Although these two species are very closely related, the infection in M. mulatta is fatal, unless aggressively treated, whereas M. fascicularis develops a chronic, but tolerable infection in the blood. As a reason for this stark difference, our analysis suggests delayed pathogen detection in M. mulatta followed by extended inflammation that eventually overwhelms this monkey’s immune response. By contrast, the natural host M. fascicularis detects the pathogen earlier and controls the inflammation. Additionally, M. fascicularis limits cell proliferation pathways during the log phase of infection, presumably in an attempt to control inflammation. Subsequent cell proliferation suggests a cell-mediated adaptive immune response. Here, we focus on molecular mechanisms underlying the key differences in the host and parasite responses and their coordination. SICAvar Type 1 surface antigens are highly correlated with pattern recognition receptor signaling and important inflammatory genes for both hosts. Analysis of pathogen detection pathways reveals a similar signaling mechanism, but with important differences in the glutamate G-protein coupled receptor (GPCR) signaling pathway. Furthermore, differences in inflammasome assembly processes suggests an important role of S100 proteins in balancing inflammation and cell proliferation. Both differences point to the importance of Ca²⁺ homeostasis in inflammation. Additionally, the kynurenine-to-tryptophan ratio, a known inflammatory biomarker, emphasizes higher inflammation in M. mulatta during log phase. Transcriptomics-aided metabolic modeling provides a functional method for evaluating these changes and understanding downstream changes in NAD metabolism and aryl hydrocarbon receptor (AhR) signaling, with enhanced NAD metabolism in M. fascicularis and stronger AhR signaling in M. mulatta. AhR signaling controls important immune genes like IL6, IFNγ and IDO1. However, direct changes due to AhR signaling could not be established due to complicated regulatory feedback mechanisms associated with the AhR repressor (AhRR). A complete understanding of the exact dynamics of the immune response is difficult to achieve. Nonetheless, our comparative analysis provides clear suggestions of processes that underlie an effective immune response. Thus, our study identifies multiple points of intervention that are apparently responsible for a balanced and effective immune response and thereby paves the way toward future immune strategies for treating malaria.

Enhanced resistance to Ca2+-induced mitochondrial permeability transition in the long-lived red-footed tortoise Chelonoidis carbonaria

The interaction between supraphysiological cytosolic Ca2+ levels and mitochondrial redox imbalance mediates the mitochondrial permeability transition (MPT). The MPT is involved in cell death, diseases and aging. This study compared the liver mitochondrial Ca2+ retention capacity and oxygen consumption in the long-lived red-footed tortoise (Chelonoidis carbonaria) with those in the rat as a reference standard. Mitochondrial Ca2+ retention capacity, a quantitative measure of MPT sensitivity, was remarkably higher in tortoises than in rats. This difference was minimized in the presence of the MPT inhibitors ADP and cyclosporine A. However, the Ca2+ retention capacities of tortoise and rat liver mitochondria were similar when both MPT inhibitors were present simultaneously. NADH-linked phosphorylating respiration rates of tortoise liver mitochondria represented only 30% of the maximal electron transport system capacity, indicating a limitation imposed by the phosphorylation system. These results suggested underlying differences in putative MPT structural components [e.g. ATP synthase, adenine nucleotide translocase (ANT) and cyclophilin D] between tortoises and rats. Indeed, in tortoise mitochondria, titrations of inhibitors of the oxidative phosphorylation components revealed a higher limitation of ANT. Furthermore, cyclophilin D activity was approximately 70% lower in tortoises than in rats. Investigation of critical properties of mitochondrial redox control that affect MPT demonstrated that tortoise and rat liver mitochondria exhibited similar rates of H2O2 release and glutathione redox status. Overall, our findings suggest that constraints imposed by ANT and cyclophilin D, putative components or regulators of the MPT pore, are associated with the enhanced resistance to Ca2+-induced MPT in tortoises.

Metabolic systems biology and multi-omics of cyanobacteria: Perspectives and future directions

Cyanobacteria are oxygenic photoautotrophs whose metabolism contains key biochemical pathways to fix atmospheric CO₂ and synthesize various metabolites. The development of bioengineering tools has enabled the manipulation of cyanobacterial chassis to produce various valuable bioproducts photosynthetically. However, effective utilization of cyanobacteria as photosynthetic cell factories needs a detailed understanding of their metabolism and its interaction with other cellular processes. Implementing systems and synthetic biology tools has generated a wealth of information on various metabolic pathways. However, to design effective engineering strategies for further improvement in growth, photosynthetic efficiency, and enhanced production of target biochemicals, in-depth knowledge of their carbon/nitrogen metabolism, pathway fluxe distribution, genetic regulation and integrative analyses are necessary. In this review, we discuss the recent advances in the development of genome-scale metabolic models (GSMMs), omics analyses (metabolomics, transcriptomics, proteomics, fluxomics), and integrative modeling approaches to showcase the current understanding of cyanobacterial metabolism.

Goals as Emergent Autopoietic Processes

While the phenomena of reaching a goal is generally represented in the framework of optimization, the phenomena of becoming of a goal is more similar to a "self-organization and emergent" rather than an "optimization and preexisting" process. In this article we provide a modeling framework for the former alternative by representing goals as emergent autopoietic structures. In order to conceptually situate our approach, we first review some of the most remarkable attempts to formally define emergence, and identify that in most cases such definitions rely on a preexisting system to be observed prior and post emergence, being thus inadequate for a formalization of emergent goals corresponding to the becoming of a systems as such (e.g. emergence of life). Next, we review how an implementation of the reaction networks framework, known as Chemical Organization Theory (COT), can be applied to formalize autopoietic structures, providing a basis to operationalize goals as an emergent process. We next revisit the definitions of emergence under the light of our approach, and demonstrate that recent taxonomies developed to classify different forms of emergence can be naturally deduced from recent work aimed to explain the kinds of changes of the organizational structure of a reaction network.

NEAT1 is essential for metabolic changes that promote breast cancer growth and metastasis

Accelerated glycolysis is the main metabolic change observed in cancer, but the underlying molecular mechanisms and their role in cancer progression remain poorly understood. Here, we show that the deletion of the long noncoding RNA (lncRNA) Neat1 in MMTV-PyVT mice profoundly impairs tumor initiation, growth, and metastasis, specifically switching off the penultimate step of glycolysis. Mechanistically, NEAT1 directly binds and forms a scaffold bridge for the assembly of PGK1/PGAM1/ENO1 complexes and thereby promotes substrate channeling for high and efficient glycolysis. Notably, NEAT1 is upregulated in cancer patients and correlates with high levels of these complexes, and genetic and pharmacological blockade of penultimate glycolysis ablates NEAT1-dependent tumorigenesis. Finally, we demonstrate that Pinin mediates glucose-stimulated nuclear export of NEAT1, through which it exerts isoform-specific and paraspeckle-independent functions. These findings establish a direct role for NEAT1 in regulating tumor metabolism, provide new insights into the Warburg effect, and identify potential targets for therapy.

Cyanobacteria as cell factories: the roles of host and pathway engineering and translational research

Cyanobacteria, a group of photoautotrophic prokaryotes, are attractive hosts for the sustainable production of chemicals from carbon dioxide and sunlight. However, the rates, yields, and titers have remained well below those needed for commercial deployment. We argue that the following areas will be central to the development of cyanobacterial cell factories: engineered and well-characterized host strains, model-guided pathway design, and advanced synthetic biology tools. Although several foundational studies report improved strain properties, translational research will be needed to develop engineered hosts and deploy them for metabolic engineering. Further, the recent developments in metabolic modeling and synthetic biology of cyanobacteria will enable nimble strategies for strain improvement with the complete cycle of design, build, test, and learn.

Two classes of functional connectivity in dynamical processes in networks

The relationship between network structure and dynamics is one of the most extensively investigated problems in the theory of complex systems of recent years. Understanding this relationship is of relevance to a range of disciplines-from neuroscience to geomorphology. A major strategy of investigating this relationship is the quantitative comparison of a representation of network architecture (structural connectivity, SC) with a (network) representation of the dynamics (functional connectivity, FC). Here, we show that one can distinguish two classes of functional connectivity-one based on simultaneous activity (co-activity) of nodes, the other based on sequential activity of nodes. We delineate these two classes in different categories of dynamical processes-excitations, regular and chaotic oscillators-and provide examples for SC/FC correlations of both classes in each of these models. We expand the theoretical view of the SC/FC relationships, with conceptual instances of the SC and the two classes of FC for various application scenarios in geomorphology, ecology, systems biology, neuroscience and socio-ecological systems. Seeing the organisation of dynamical processes in a network either as governed by co-activity or by sequential activity allows us to bring some order in the myriad of observations relating structure and function of complex networks.

Age-related susceptibility to insulin resistance arises from a combination of CPT1B decline and lipid overload

BACKGROUND

The skeletal muscle plays a central role in glucose homeostasis through the uptake of glucose from the extracellular medium in response to insulin. A number of factors are known to disrupt the normal response to insulin leading to the emergence of insulin resistance (IR). Advanced age and a high-fat diet are factors that increase the susceptibility to IR, with lipid accumulation in the skeletal muscle being a key driver of this phenomenon. It is debated, however, whether lipid accumulation arises due to dietary lipid overload or from a decline of mitochondrial function. To gain insights into the interplay of diet and age in the flexibility of muscle lipid and glucose handling, we combined lipidomics, proteomics, mitochondrial function analysis and computational modelling to investigate young and aged mice on a low- or high-fat diet (HFD).

RESULTS

As expected, aged mice were more susceptible to IR when given a HFD than young mice. The HFD induced intramuscular lipid accumulation specifically in aged mice, including C18:0-containing ceramides and diacylglycerols. This was reflected by the mitochondrial β-oxidation capacity, which was upregulated by the HFD in young, but not in old mice. Conspicuously, most β-oxidation proteins were upregulated by the HFD in both groups, but carnitine palmitoyltransferase 1B (CPT1B) declined in aged animals. Computational modelling traced the flux control mostly to CPT1B, suggesting a CPT1B-driven loss of flexibility to the HFD with age. Finally, in old animals, glycolytic protein levels were reduced and less flexible to the diet.

CONCLUSION

We conclude that intramuscular lipid accumulation and decreased insulin sensitivity are not due to age-related mitochondrial dysfunction or nutritional overload alone, but rather to their combined effects. Moreover, we identify CPT1B as a potential target to counteract age-dependent intramuscular lipid accumulation and thereby IR.

Integrative Methods for Studying Cardiac Energetics

The more recent studies of human pathologies have essentially revealed the complexity of the interactions involved at the different levels of integration in organ physiology. Integrated organ thus reveals functional properties not predictable by underlying molecular events. It is therefore obvious that current fine molecular analyses of pathologies should be fruitfully combined with integrative approaches of whole organ function. It follows that an important issue in the comprehension of the link between molecular events in pathologies and whole organ function/dysfunction is the development of new experimental strategies aimed at the study of the integrated organ physiology. Cardiovascular diseases are a good example as heart submitted to ischemic conditions has to cope both with a decreased supply of nutrients and oxygen, and the necessary increased activity required to sustain whole body-including the heart itself-oxygenation.By combining the principles of control analysis with noninvasive ³¹P NMR measurement of the energetic intermediates and simultaneous measurement of heart contractile activity, we developed MoCA (for Modular Control and regulation Analysis), an integrative approach designed to study in situ control and regulation of cardiac energetics during contraction in intact beating perfused isolated heart (Diolez et al., Am J Physiol Regul Integr Comp Physiol 293(1):R13-R19, 2007). Because it gives real access to integrated organ function, MoCA brings out a new type of information-the "elasticities," referring to integrated internal responses to metabolic changes-that may be a key to the understanding of the processes involved in pathologies. MoCA can potentially be used not only to detect the origin of the defects associated with the pathology, but also to provide the quantitative description of the routes by which these defects-or also drugs-modulate global heart function, therefore opening therapeutic perspectives. This review presents selected examples of the applications to isolated intact beating heart that evidence different modes of energetic regulation of cardiac contraction. We also discuss the clinical application by using noninvasive ³¹P cardiac energetics examination under clinical conditions for detection of heart pathologies.

Resource uptake and the evolution of moderately efficient enzymes

Enzymes speed up reactions that would otherwise be too slow to sustain the metabolism of selfreplicators. Yet, most enzymes seem only moderately efficient, exhibiting kinetic parameters orders of magnitude lower than their expected physically achievable maxima and spanning over surprisingly large ranges of values. Here, we question how these parameters evolve using a mechanistic model where enzyme efficiency is a key component of individual competition for resources. We show that kinetic parameters are under strong directional selection only up to a point, above which enzymes appear to evolve under near-neutrality, thereby confirming the qualitative observation of other modeling approaches. While the existence of a large fitness plateau could potentially explain the extensive variation in enzyme features reported, we show using a population genetics model that such a widespread distribution is an unlikely outcome of evolution on a common landscape, as mutation–selection–drift balance occupy a narrow area even when very moderate biases towards lower efficiency are considered. Instead, differences in the evolutionary context encountered by each enzyme should be involved, such that each evolves on an individual, unique landscape. Our results point to drift and effective population size playing an important role, along with the kinetics of nutrient transporters, the tolerance to high concentrations of intermediate metabolites, and the reversibility of reactions. Enzyme concentration also shapes selection on kinetic parameters, but we show that the joint evolution of concentration and efficiency does not yield extensive variance in evolutionary outcomes when documented costs to protein expression are applied.

Epitope-Based Peptide Vaccine Design against Fructose Bisphosphate Aldolase of Candida glabrata: An Immunoinformatics Approach

Background

Candida glabrata is a human opportunistic pathogen that can cause life-threatening systemic infections. Although there are multiple effective vaccines against fungal infections and some of these vaccines are engaged in different stages of clinical trials, none of them have yet been approved by the FDA.

Aim

Using immunoinformatics approach to predict the most conserved and immunogenic B- and T-cell epitopes from the fructose bisphosphate aldolase (Fba1) protein of C. glabrata. Material and Method. 13 C. glabrata fructose bisphosphate aldolase protein sequences (361 amino acids) were retrieved from NCBI and presented in several tools on the IEDB server for prediction of the most promising epitopes. Homology modeling and molecular docking were performed.

Result

The promising B-cell epitopes were AYFKEH, VDKESLYTK, and HVDKESLYTK, while the promising peptides which have high affinity to MHC I binding were AVHEALAPI, KYFKRMAAM, QTSNGGAAY, RMAAMNQWL, and YFKEHGEPL. Two peptides, LFSSHMLDL and YIRSIAPAY, were noted to have the highest affinity to MHC class II that interact with 9 alleles. The molecular docking revealed that the epitopes QTSNGGAAY and LFSSHMLDL have the lowest binding energy to MHC molecules.

Conclusion

The epitope-based vaccines predicted by using immunoinformatics tools have remarkable advantages over the conventional vaccines in that they are more specific, less time consuming, safe, less allergic, and more antigenic. Further in vivo and in vitro experiments are needed to prove the effectiveness of the best candidate's epitopes (QTSNGGAAY and LFSSHMLDL). To the best of our knowledge, this is the first study that has predicted B- and T-cell epitopes from the Fba1 protein by using in silico tools in order to design an effective epitope-based vaccine against C. glabrata.

The role of GDP-l-galactose phosphorylase in the control of ascorbate biosynthesis

The enzymes involved in l-ascorbate biosynthesis in photosynthetic organisms (the Smirnoff-Wheeler [SW] pathway) are well established. Here, we analyzed their subcellular localizations and potential physical interactions and assessed their role in the control of ascorbate synthesis. Transient expression of C terminal-tagged fusions of SW genes in Nicotiana benthamiana and Arabidopsis thaliana mutants complemented with genomic constructs showed that while GDP-d-mannose epimerase is cytosolic, all the enzymes from GDP-d-mannose pyrophosphorylase (GMP) to l-galactose dehydrogenase (l-GalDH) show a dual cytosolic/nuclear localization. All transgenic lines expressing functional SW protein green fluorescent protein fusions driven by their endogenous promoters showed a high accumulation of the fusion proteins, with the exception of those lines expressing GDP-l-galactose phosphorylase (GGP) protein, which had very low abundance. Transient expression of individual or combinations of SW pathway enzymes in N. benthamiana only increased ascorbate concentration if GGP was included. Although we did not detect direct interaction between the different enzymes of the pathway using yeast-two hybrid analysis, consecutive SW enzymes, as well as the first and last enzymes (GMP and l-GalDH) associated in coimmunoprecipitation studies. This association was supported by gel filtration chromatography, showing the presence of SW proteins in high-molecular weight fractions. Finally, metabolic control analysis incorporating known kinetic characteristics showed that previously reported feedback repression at the GGP step, combined with its relatively low abundance, confers a high-flux control coefficient and rationalizes why manipulation of other enzymes has little effect on ascorbate concentration.

Lineage-specific selection and the evolution of virulence in the Candida clade

Of the many microbial species on earth, only a small number are able to thrive in humans and cause disease. Comparison of closely related pathogenic and nonpathogenic species can therefore be useful in identifying key features that contribute to virulence. We created interspecies hybrids between Candida albicans, a prevalent fungal pathogen of humans, and Candida dubliniensis, a close, but much less pathogenic, relative. By comparing genome-wide expression differences between the two genomes in the same cell, we surmised that since the two species diverged from a common ancestor, natural selection has acted upon the expression level of an ancient metabolic pathway, illustrating that pathogenicity traits can arise over evolutionary timescales through small expression changes in deeply conserved proteins.

Candida albicans is the most common cause of systemic fungal infections in humans and is considerably more virulent than its closest known relative, Candida dubliniensis. To investigate this difference, we constructed interspecies hybrids and quantified mRNA levels produced from each genome in the hybrid. This approach systematically identified expression differences in orthologous genes arising from cis-regulatory sequence changes that accumulated since the two species last shared a common ancestor, some 10 million y ago. We documented many orthologous gene-expression differences between the two species, and we pursued one striking observation: All 15 genes coding for the enzymes of glycolysis showed higher expression from the C. albicans genome than the C. dubliniensis genome in the interspecies hybrid. This pattern requires evolutionary changes to have occurred at each gene; the fact that they all act in the same direction strongly indicates lineage-specific natural selection as the underlying cause. To test whether these expression differences contribute to virulence, we created a C. dubliniensis strain in which all 15 glycolysis genes were produced at modestly elevated levels and found that this strain had significantly increased virulence in the standard mouse model of systemic infection. These results indicate that small expression differences across a deeply conserved set of metabolism enzymes can play a significant role in the evolution of virulence in fungal pathogens.

Modelling Cell Metabolism: A Review on Constraint-Based Steady-State and Kinetic Approaches

Studying cell metabolism serves a plethora of objectives such as the enhancement of bioprocess performance, and advancement in the understanding of cell biology, of drug target discovery, and in metabolic therapy. Remarkable successes in these fields emerged from heuristics approaches, for instance, with the introduction of effective strategies for genetic modifications, drug developments and optimization of bioprocess management. However, heuristics approaches have showed significant shortcomings, such as to describe regulation of metabolic pathways and to extrapolate experimental conditions. In the specific case of bioprocess management, such shortcomings limit their capacity to increase product quality, while maintaining desirable productivity and reproducibility levels. For instance, since heuristics approaches are not capable of prediction of the cellular functions under varying experimental conditions, they may lead to sub-optimal processes. Also, such approaches used for bioprocess control often fail in regulating a process under unexpected variations of external conditions. Therefore, methodologies inspired by the systematic mathematical formulation of cell metabolism have been used to address such drawbacks and achieve robust reproducible results. Mathematical modelling approaches are effective for both the characterization of the cell physiology, and the estimation of metabolic pathways utilization, thus allowing to characterize a cell population metabolic behavior. In this article, we present a review on methodology used and promising mathematical modelling approaches, focusing primarily to investigate metabolic events and regulation. Proceeding from a topological representation of the metabolic networks, we first present the metabolic modelling approaches that investigate cell metabolism at steady state, complying to the constraints imposed by mass conservation law and thermodynamics of reactions reversibility. Constraint-based models (CBMs) are reviewed highlighting the set of assumed optimality functions for reaction pathways. We explore models simulating cell growth dynamics, by expanding flux balance models developed at steady state. Then, discussing a change of metabolic modelling paradigm, we describe dynamic kinetic models that are based on the mathematical representation of the mechanistic description of nonlinear enzyme activities. In such approaches metabolic pathway regulations are considered explicitly as a function of the activity of other components of metabolic networks and possibly far from the metabolic steady state. We have also assessed the significance of metabolic model parameterization in kinetic models, summarizing a standard parameter estimation procedure frequently employed in kinetic metabolic modelling literature. Finally, some optimization practices used for the parameter estimation are reviewed.

From insulin to Akt: Time delays and dominant processes

Akt/PKB regulates numerous processes in the mammalian cell, including cell survival and proliferation, and glucose uptake in response to insulin. Abnormalities in Akt signalling are linked to the development of Type 2 diabetes, cardio-vascular disease, and cancer. In the absence of insulin, Akt is predominantly found in the inactive state in the cytosol. Following insulin stimulation, Akt translocates to the plasma membrane, docks, and is phosphorylated to take on the active conformation. In turn, the activated Akt travels to and phosphorylates its many downstream substrates. Although crucial to the activation process, the translocation of Akt from the cytosol to the plasma membrane is currently not well understood. Here we detail the parameter optimisation of a mathematical model of Akt translocation to experimental data. We have quantified the time delay between the application of insulin and the downstream Akt translocation response, indicating the constraints on the timing of the intermediate processes. A delay of approximately 0.4 min prior to the Akt response was determined for the application of 1 nM insulin to cells in the basal state, whereas it was found that a further transition from physiological insulin to higher stimuli did not incur a delay. Furthermore, our investigation indicates that the dominant processes regulating the appearance of Akt at the plasma membrane differ with the insulin level. For physiological insulin, the rate limiting step was the release of Akt to the plasma membrane in response to the insulin signal. In contrast, at high insulin levels, regulation of the recycling of Akt from the plasma membrane to the cytosol was also required.

Identification of flux checkpoints in a metabolic pathway through white-box, grey-box and black-box modeling approaches

Metabolic pathway modeling plays an increasing role in drug design by allowing better understanding of the underlying regulation and controlling networks in the metabolism of living organisms. However, despite rapid progress in this area, pathway modeling can become a real nightmare for researchers, notably when few experimental data are available or when the pathway is highly complex. Here, three different approaches were developed to model the second part of glycolysis of E. histolytica as an application example, and have succeeded in predicting the final pathway flux: one including detailed kinetic information (white-box), another with an added adjustment term (grey-box) and the last one using an artificial neural network method (black-box). Afterwards, each model was used for metabolic control analysis and flux control coefficient determination. The first two enzymes of this pathway are identified as the key enzymes playing a role in flux control. This study revealed the significance of the three methods for building suitable models adjusted to the available data in the field of metabolic pathway modeling, and could be useful to biologists and modelers.

Metabolic pathway engineering: Perspectives and applications

BACKGROUND

Metabolic engineering aims at contriving microbes as biocatalysts for enhanced and cost-effective production of countless secondary metabolites. These secondary metabolites can be treated as the resources of industrial chemicals, pharmaceuticals and fuels. Plants are also crucial targets for metabolic engineers to produce necessary secondary metabolites. Metabolic engineering of both microorganism and plants also contributes towards drug discovery. In order to implement advanced metabolic engineering techniques efficiently, metabolic engineers should have detailed knowledge about cell physiology and metabolism. Principle behind methodologies: Genome-scale mathematical models of integrated metabolic, signal transduction, gene regulatory and protein-protein interaction networks along with experimental validation can provide such knowledge in this context. Incorporation of omics data into these models is crucial in the case of drug discovery. Inverse metabolic engineering and metabolic control analysis (MCA) can help in developing such models. Artificial intelligence methodology can also be applied for efficient and accurate metabolic engineering.

CONCLUSION

In this review, we discuss, at the beginning, the perspectives of metabolic engineering and its application on microorganism and plant leading to drug discovery. At the end, we elaborate why inverse metabolic engineering and MCA are closely related to modern metabolic engineering. In addition, some crucial steps ensuring efficient and optimal metabolic engineering strategies have been discussed. Moreover, we explore the use of genomics data for the activation of silent metabolic clusters and how it can be integrated with metabolic engineering. Finally, we exhibit a few applications of artificial intelligence to metabolic engineering.

Towards model-driven characterization and manipulation of plant lipid metabolism

Plant lipids have versatile applications and provide essential fatty acids in human diet. Therefore, there has been a growing interest to better characterize the genetic basis, regulatory networks, and metabolic pathways that shape lipid quantity and composition. Addressing these issues is challenging due to context-specificity of lipid metabolism integrating environmental, developmental, and tissue-specific cues. Here we systematically review the known metabolic pathways and regulatory interactions that modulate the levels of storage lipids in oilseeds. We argue that the current understanding of lipid metabolism provides the basis for its study in the context of genome-wide plant metabolic networks with the help of approaches from constraint-based modeling and metabolic flux analysis. The focus is on providing a comprehensive summary of the state-of-the-art of modeling plant lipid metabolic pathways, which we then contrast with the existing modeling efforts in yeast and microalgae. We then point out the gaps in knowledge of lipid metabolism, and enumerate the recent advances of using genome-wide association and quantitative trait loci mapping studies to unravel the genetic regulations of lipid metabolism. Finally, we offer a perspective on how advances in the constraint-based modeling framework can propel further characterization of plant lipid metabolism and its rational manipulation.

Metabolic control analysis of hepatic glycogen synthesis in vivo

Hepatic glycogen synthesis plays a critical role in maintaining normal glucose homeostasis; however, the rate-controlling step regulating this process is unknown. Applying metabolic control analysis in vivo, we show that the regulation of insulin-stimulated hepatic glycogen synthesis under both normal and pathophysiological conditions of fatty liver-associated hepatic insulin resistance is controlled at the glucokinase (GCK) step through GCK translocation.

Multiple insulin-regulated enzymes participate in hepatic glycogen synthesis, and the rate-controlling step responsible for insulin stimulation of glycogen synthesis is unknown. We demonstrate that glucokinase (GCK)-mediated glucose phosphorylation is the rate-controlling step in insulin-stimulated hepatic glycogen synthesis in vivo, by use of the somatostatin pancreatic clamp technique using [¹³C₆]glucose with metabolic control analysis (MCA) in three rat models: 1) regular chow (RC)-fed male rats (control), 2) high fat diet (HFD)-fed rats, and 3) RC-fed rats with portal vein glucose delivery at a glucose infusion rate matched to the control. During hyperinsulinemia, hyperglycemia dose-dependently increased hepatic glycogen synthesis. At similar levels of hyperinsulinemia and hyperglycemia, HFD-fed rats exhibited a decrease and portal delivery rats exhibited an increase in hepatic glycogen synthesis via the direct pathway compared with controls. However, the strong correlation between liver glucose-6-phosphate concentration and net hepatic glycogen synthetic rate was nearly identical in these three groups, suggesting that the main difference between models is the activation of GCK. MCA yielded a high control coefficient for GCK in all three groups. We confirmed these findings in studies of hepatic GCK knockdown using an antisense oligonucleotide. Reduced liver glycogen synthesis in lipid-induced hepatic insulin resistance and increased glycogen synthesis during portal glucose infusion were explained by concordant changes in translocation of GCK. Taken together, these data indicate that the rate of insulin-stimulated hepatic glycogen synthesis is controlled chiefly through GCK translocation.

Computational systems biology of cellular processes in Arabidopsis thaliana: an overview

Systems biology strives for gaining an understanding of biological phenomena by studying the interactions of different parts of a system and integrating the knowledge obtained into the current view of the underlying processes. This is achieved by a tight combination of quantitative experimentation and computational modeling. While there is already a large quantity of systems biology studies describing human, animal and especially microbial cell biological systems, plant biology has been lagging behind for many years. However, in the case of the model plant Arabidopsis thaliana, the steadily increasing amount of information on the levels of its genome, proteome and on a variety of its metabolic and signalling pathways is progressively enabling more researchers to construct models for cellular processes for the plant, which in turn encourages more experimental data to be generated, showing also for plant sciences how fruitful systems biology research can be. In this review, we provide an overview over some of these recent studies which use different systems biological approaches to get a better understanding of the cell biology of A. thaliana. The approaches used in these are genome-scale metabolic modeling, as well as kinetic modeling of metabolic and signalling pathways. Furthermore, we selected several cases to exemplify how the modeling approaches have led to significant advances or new perspectives in the field.

Investigation of the methylerythritol 4-phosphate pathway for microbial terpenoid production through metabolic control analysis

BACKGROUND

Terpenoids are of high interest as chemical building blocks and pharmaceuticals. In microbes, terpenoids can be synthesized via the methylerythritol phosphate (MEP) or mevalonate (MVA) pathways. Although the MEP pathway has a higher theoretical yield, metabolic engineering has met with little success because the regulation of the pathway is poorly understood.

RESULTS

We applied metabolic control analysis to the MEP pathway in Escherichia coli expressing a heterologous isoprene synthase gene (ispS). The expression of ispS led to the accumulation of isopentenyl pyrophosphate (IPP)/dimethylallyl pyrophosphate (DMAPP) and severely impaired bacterial growth, but the coexpression of ispS and isopentenyl diphosphate isomerase (idi) restored normal growth and wild-type IPP/DMAPP levels. Targeted proteomics and metabolomics analysis provided a quantitative description of the pathway, which was perturbed by randomizing the ribosome binding site in the gene encoding 1-deoxyxylulose 5-phosphate synthase (Dxs). Dxs has a flux control coefficient of 0.35 (i.e., a 1% increase in Dxs activity resulted in a 0.35% increase in pathway flux) in the isoprene-producing strain and therefore exerted significant control over the flux though the MEP pathway. At higher dxs expression levels, the intracellular concentration of 2-C-methyl-D-erythritol-2,4-cyclopyrophosphate (MEcPP) increased substantially in contrast to the other MEP pathway intermediates, which were linearly dependent on the abundance of Dxs. This indicates that 4-hydroxy-3-methylbut-2-en-1-yl diphosphate synthase (IspG), which consumes MEcPP, became saturated and therefore limited the flux towards isoprene. The higher intracellular concentrations of MEcPP led to the efflux of this intermediate into the growth medium.

DISCUSSION

These findings show the importance of Dxs, Idi and IspG and metabolite export for metabolic engineering of the MEP pathway and will facilitate further approaches for the microbial production of valuable isoprenoids.

Mechanistic inferences on metabolic dysfunction in posttraumatic stress disorder from an integrated model and multiomic analysis: role of glucocorticoid receptor sensitivity

Posttraumatic stress disorder (PTSD) is associated with neuroendocrine alterations and metabolic abnormalities; however, how metabolism is affected by neuroendocrine disturbances is unclear. The data from combat-exposed veterans with PTSD show increased glycolysis to lactate flux, reduced TCA cycle flux, impaired amino acid and lipid metabolism, insulin resistance, inflammation, and hypersensitive hypothalamic-pituitary-adrenal (HPA) axis. To analyze whether the co-occurrence of multiple metabolic abnormalities is independent or arises from an underlying regulatory defect, we employed a systems biological approach using an integrated mathematical model and multiomic analysis. The models for hepatic metabolism, HPA axis, inflammation, and regulatory signaling were integrated to perform metabolic control analysis (MCA) with respect to the observations from our clinical data. We combined the metabolomics, neuroendocrine, clinical laboratory, and cytokine data from combat-exposed veterans with and without PTSD to characterize the differences in regulatory effects. MCA revealed mechanistic association of the HPA axis and inflammation with metabolic dysfunction consistent with PTSD. This was supported by the data using correlational and causal analysis that revealed significant associations between cortisol suppression, high-sensitivity C-reactive protein, homeostatic model assessment of insulin resistance, γ-glutamyltransferase, hypoxanthine, and several metabolites. Causal mediation analysis indicates that the effects of enhanced glucocorticoid receptor sensitivity (GRS) on glycolytic pathway, gluconeogenic and branched-chain amino acids, triglycerides, and hepatic function are jointly mediated by inflammation, insulin resistance, oxidative stress, and energy deficit. Our analysis suggests that the interventions to normalize GRS and inflammation may help to manage features of metabolic dysfunction in PTSD.

Metabolic model guided strain design of cyanobacteria

Cyanobacteria are oxygenic photoautotrophs that serve as potential platforms for the production of biochemicals from cheap and renewable raw materials - sunlight, water, and carbon dioxide. Systems level analysis of the metabolic network of these organisms could enable the successful engineering of these organisms for the enhanced production of target chemicals. Metabolic modeling techniques including both stoichiometric and kinetic modeling with a genome-wide coverage enable a global assessment of metabolic capabilities. Recent studies guided by such modeling techniques have engineered strains for the enhanced production of valuable chemicals such as ethanol, n-butanol, 1,3-propanediol, glycerol, limonene, and isoprene from CO₂.

Seed quality and carbon primary metabolism

Improving seed quality is amongst the most important challenges of contemporary agriculture. In fact, using plant varieties with better germination rates that are more tolerant to stress during seedling establishment may improve crop yield considerably. Therefore, intense efforts are currently being devoted to improve seed quality in many species, mostly using genomics tools. However, despite its considerable importance during seed imbibition and germination processes, primary carbon metabolism in seeds is less studied. Our knowledge of the physiology of seed respiration and energy generation and the impact of these processes on seed performance have made limited progress over the past three decades. In particular, (isotope-assisted) metabolomics of seeds has only been assessed occasionally, and there is limited information on possible quantitative relationships between metabolic fluxes and seed quality. Here, we review the recent literature and provide an overview of potential links between metabolic efficiency, metabolic biomarkers, and seed quality and discuss implications for future research, including a climate change context.

On the use of oxygenic photosynthesis for the sustainable production of commodity chemicals

A sustainable society will have to largely refrain from the use of fossil carbon deposits. In such a regime, renewable electricity can be harvested as a primary source of energy. However, as for the synthesis of carbon-based materials from bulk chemicals, an alternative is required. A sustainable approach towards this is the synthesis of commodity chemicals from CO₂ , water and sunlight. Multiple paths to achieve this have been designed and tested in the domains of chemistry and biology. In the latter, the use of both chemotrophic and phototrophic organisms has been advocated. 'Direct conversion' of CO₂ and H₂ O, catalyzed by an oxyphototroph, has excellent prospects to become the most economically competitive of these transformations, because of the relative ease of scale-up of this process. Significantly, for a wide range of energy and commodity products, a proof of principle via engineering of the corresponding production organism has been provided. In the optimization of a cyanobacterial production organism, a wide range of aspects has to be addressed. Of these, here we will put our focus on: (1) optimizing the (carbon) flux to the desired product; (2) increasing the genetic stability of the producing organism and (3) maximizing its energy conversion efficiency. Significant advances have been made on all these three aspects during the past 2 years and these will be discussed: (1) increasing the carbon partitioning to >50%; (2) aligning product formation with the growth of the cells and (3) expanding the photosynthetically active radiation region for oxygenic photosynthesis.