Metabolic control analysis: a tool for designing strategies to manipulate metabolic pathways

Acetylation of PGK1 at lysine 323 promotes glycolysis, cell proliferation, and metastasis in luminal A breast cancer cells

BACKGROUND

In prior research employing iTRAQ (Isobaric Tags for Relative and Absolute Quantitation) technology, we identified a range of proteins in breast cancer tissues exhibiting high levels of acetylation. Despite this advancement, the specific functions and implications of these acetylated proteins in the context of cancer biology have yet to be elucidated. This study aims to systematically investigate the functional roles of these acetylated proteins with the objective of identifying potential therapeutic targets within breast cancer pathophysiology.

METHODS

Acetylated targets were identified through bioinformatics, with their expression and acetylation subsequently confirmed. Proteomic analysis and validation studies identified potential acetyltransferases and deacetylases. We evaluated metabolic functions via assays for catalytic activity, glucose consumption, ATP levels, and lactate production. Cell proliferation and metastasis were assessed through viability, cycle analysis, clonogenic assays, PCNA uptake, wound healing, Transwell assays, and MMP/EMT marker detection.

RESULTS

Acetylated proteins in breast cancer were primarily involved in metabolism, significantly impacting glycolysis and the tricarboxylic acid cycle. Notably, PGK1 showed the highest acetylation at lysine 323 and exhibited increased expression and acetylation across breast cancer tissues, particularly in T47D and MCF-7 cells. Notably, 18 varieties acetyltransferases or deacetylases were identified in T47D cells, among which p300 and Sirtuin3 were validated for their interaction with PGK1. Acetylation at 323 K enhanced PGK1's metabolic role by boosting its activity, glucose uptake, ATP production, and lactate output. This modification also promoted cell proliferation, as evidenced by increased viability, S phase ratio, clonality, and PCNA levels. Furthermore, PGK1-323 K acetylation facilitated metastasis, improving wound healing, cell invasion, and upregulating MMP2, MMP9, N-cadherin, and Vimentin while downregulating E-cadherin.

CONCLUSION

PGK1-323 K acetylation was significantly elevated in T47D and MCF-7 luminal A breast cancer cells and this acetylation could be regulated by p300 and Sirtuin3. PGK1-323 K acetylation promoted cell glycolysis, proliferation, and metastasis, highlighting novel epigenetic targets for breast cancer therapy.

Metabolomic profiling reveals altered phenylalanine metabolism in Parkinson's disease in an Egyptian cohort

Introduction: Parkinson's disease (PD) is the most common motor neurodegenerative disease worldwide. Given the complexity of PD etiology and the different metabolic derangements correlated to the disease, metabolomics profiling of patients is a helpful tool to identify patho-mechanistic pathways for the disease development. Dopamine metabolism has been the target of several previous studies, of which some have reported lower phenylalanine and tyrosine levels in PD patients compared to controls. Methods: In this study, we have collected plasma from 27 PD patients, 18 reference controls, and 8 high-risk controls to perform a metabolomic study using liquid chromatography-electrospray ionization-tandem mass spectrometry (LC-ESI-MS/MS). Results: Our findings revealed higher intensities of trans-cinnamate, a phenylalanine metabolite, in patients compared to reference controls. Thus, we hypothesize that phenylalanine metabolism has been shifted to produce trans-cinnamate via L-phenylalanine ammonia lyase (PAL), instead of producing tyrosine, a dopamine precursor, via phenylalanine hydroxylase (PAH). Discussion: Given that these metabolites are precursors to several other metabolic pathways, the intensities of many metabolites such as dopamine, norepinephrine, and 3-hydroxyanthranilic acid, which connects phenylalanine metabolism to that of tryptophan, have been altered. Consequently, and in respect to Metabolic Control Analysis (MCA) theory, the levels of tryptophan metabolites have also been altered. Some of these metabolites are tryptamine, melatonin, and nicotinamide. Thus, we assume that these alterations could contribute to the dopaminergic, adrenergic, and serotonergic neurodegeneration that happen in the disease.

Segmentation and evaluation of pathway module efficiency: Quantitative approach to monitor and overcome evolving bottlenecks in xylose to ethanol pathway

Engineering microbes that can efficiently ferment xylose to ethanol is critical to the development of renewable fuels from lignocellulosic biomass. To accelerate the strain optimization process, a method termed Segmentation and Evaluation of Pathway Module Efficiency (SEPME) was developed to enable rapid and iterative identification and removal of metabolic bottlenecks. Using SEPME, the overall pathway was segmented into two modules: the upstream xylose assimilation pathway and the downstream pentose phosphate pathway, glycolysis, and fermentation. The efficiencies of both modules were then quantified to identify the rate controlling module, followed by analyses of control coefficients, reaction rates, and byproduct concentrations to narrow down targets within the module. SEPME analysis revealed that as the strain was engineered with increasing xylose-to-ethanol yields, the bottlenecks shifted within a module and across the two modules. Guided by SEPME, these bottlenecks were removed one by one, and a strain approaching the theoretical ethanol yield was obtained.

Creating new-to-nature carbon fixation: A guide

Synthetic biology aims at designing new biological functions from first principles. These new designs allow to expand the natural solution space and overcome the limitations of naturally evolved systems. One example is synthetic CO2-fixation pathways that promise to provide more efficient ways for the capture and conversion of CO2 than natural pathways, such as the Calvin Benson Bassham (CBB) cycle of photosynthesis. In this review, we provide a practical guideline for the design and realization of such new-to-nature CO2-fixation pathways. We introduce the concept of "synthetic CO2-fixation", and give a general overview over the enzymology and topology of synthetic pathways, before we derive general principles for their design from their eight naturally evolved analogs. We provide a comprehensive summary of synthetic carbon-assimilation pathways and derive a step-by-step, practical guide from the theoretical design to their practical implementation, before ending with an outlook on new developments in the field.

Exerkines, Nutrition, and Systemic Metabolism

The cornerstones of good health are exercise, proper food, and sound nutrition. Physical exercise should be a lifelong routine, supported by proper food selections to satisfy nutrient requirements based on energy needs, energy management, and variety to achieve optimal metabolism and physiology. The human body is sustained by intermediary and systemic metabolism integrating the physiologic processes for cells, tissues, organs, and systems. Recently, interest in specific metabolites, growth factors, cytokines, and hormones called exerkines has emerged to explain cooperation between nutrient supply organs and the brain during exercise. Exerkines consist of different compounds described as signaling moiety released during and after exercise. Examples of exerkines include oxylipin 12, 13 diHOME, lipid hormone adiponectin, growth factor BDNF, metabolite lactate, reactive oxygen species (ROS), including products of fatty acid oxidation, and cytokines such as interleukin-6. At this point, it is believed that exerkines are immediate, fast, and long-lasting factors resulting from exercise to support body energy needs with an emphasis on the brain. Although exerkines that are directly a product of macronutrient metabolism such as lactate, and result from catabolism is not surprising. Furthermore, other metabolites of macronutrient metabolism seem to be candidate exerkines. The exerkines originate from muscle, adipose, and liver and support brain metabolism, energy, and physiology. The purpose of this review is to integrate the actions of exerkines with respect to metabolism that occurs during exercise and propose other participating factors of exercise and brain physiology. The role of diet and macronutrients that influence metabolism and, consequently, the impact of exercise will be discussed. This review will also describe the evidence for PUFA, their metabolic and physiologic derivatives endocannabinoids, and oxylipins that validate them being exerkines. The intent is to present additional insights to better understand exerkines with respect to systemic metabolism.

Metabolic Rewiring of Mycobacterium tuberculosis upon Drug Treatment and Antibiotics Resistance

Tuberculosis (TB), caused by Mycobacterium tuberculosis (Mtb), remains a significant global health challenge, further compounded by the issue of antimicrobial resistance (AMR). AMR is a result of several system-level molecular rearrangements enabling bacteria to evolve with better survival capacities: metabolic rewiring is one of them. In this review, we present a detailed analysis of the metabolic rewiring of Mtb in response to anti-TB drugs and elucidate the dynamic mechanisms of bacterial metabolism contributing to drug efficacy and resistance. We have discussed the current state of AMR, its role in the prevalence of the disease, and the limitations of current anti-TB drug regimens. Further, the concept of metabolic rewiring is defined, underscoring its relevance in understanding drug resistance and the biotransformation of drugs by Mtb. The review proceeds to discuss the metabolic adaptations of Mtb to drug treatment, and the pleiotropic effects of anti-TB drugs on Mtb metabolism. Next, the association between metabolic changes and antimycobacterial resistance, including intrinsic and acquired drug resistance, is discussed. The review concludes by summarizing the challenges of anti-TB treatment from a metabolic viewpoint, justifying the need for this discussion in the context of novel drug discovery, repositioning, and repurposing to control AMR in TB.

Control Theory and Systems Biology: Potential Applications in Neurodegeneration and Search for Therapeutic Targets

Control theory, a well-established discipline in engineering and mathematics, has found novel applications in systems biology. This interdisciplinary approach leverages the principles of feedback control and regulation to gain insights into the complex dynamics of cellular and molecular networks underlying chronic diseases, including neurodegeneration. By modeling and analyzing these intricate systems, control theory provides a framework to understand the pathophysiology and identify potential therapeutic targets. Therefore, this review examines the most widely used control methods in conjunction with genomic-scale metabolic models in the steady state of the multi-omics type. According to our research, this approach involves integrating experimental data, mathematical modeling, and computational analyses to simulate and control complex biological systems. In this review, we find that the most significant application of this methodology is associated with cancer, leaving a lack of knowledge in neurodegenerative models. However, this methodology, mainly associated with the Minimal Dominant Set (MDS), has provided a starting point for identifying therapeutic targets for drug development and personalized treatment strategies, paving the way for more effective therapies.

Metabolic control analysis of the transsulfuration pathway and the compensatory role of the cysteine transport in Trypanosoma cruzi

Trypanosoma cruzi is the causal agent of American Trypanosomiasis or Chagas Disease in humans. The current drugs for its treatment benznidazole and nifurtimox have inconveniences of toxicity and efficacy; therefore, the search for new therapies continues. Validation through genetic strategies of new drug targets against the parasite metabolism have identified numerous essential genes. Target validation can be further narrowed by applying Metabolic Control Analysis (MCA) to determine the flux control coefficients of the pathway enzymes. That coefficient is a quantitative value that represents the degree in which an enzyme/transporter determines the flux of a metabolic pathway; those with the highest coefficients can be promising drug targets. Previous studies have demonstrated that cysteine (Cys) is a key precursor for the synthesis of trypanothione, the main antioxidant metabolite in the parasite. In this research, MCA was applied in an ex vivo system to the enzymes of the reverse transsulfuration pathway (RTP) for Cys synthesis composed by cystathionine beta synthase (CBS) and cystathionine gamma lyase (CGL). The results indicated that CGL has 90% of the control of the pathway flux. Inhibition of CGL with propargylglycine (PAG) decreased the levels of Cys and trypanothione and depleted those of glutathione in epimastigotes (proliferative stage in the insect vector); these metabolite changes were prevented by supplementing with Cys, suggesting a compensatory role of the Cys transport (CysT). Indeed, Cys supplementation (but not PAG treatment) increased the activity of the CysT in epimastigotes whereas in trypomastigotes (infective stage in mammals) CysT was increased when they were incubated with PAG. Our results suggested that CGL could be a potential drug target given its high control on the RTP flux and its effects on the parasite antioxidant defense. However, the redundant Cys supply pathways in the parasite may require inhibition of the CysT as well. Our findings also suggest differential responses of the Cys supply pathways in different parasite stages.

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.

Diving into cancer OXPHOS - The application of metabolic control analysis to cell and tissue research

Knowing how the oxidative phosphorylation (OXPHOS) system in cancer cells operates differently from that of normal cells would help find compounds that specifically paralyze the energy metabolism of cancer cells. The first experiments in the study of mitochondrial respiration using the metabolic control analysis (MCA) method were done with isolated liver mitochondria in the early 80s of the last century. Subsequent studies have shown that the regulation of mitochondrial respiration by ADP in isolated mitochondria differs significantly from a model of mitochondria in situ, where the contacts with components in the cytoplasm are largely preserved. The method of selective permeabilization of the outer membrane of the cells allows the application of MCA to evaluate the contribution of different components of the OXPHOS system to its functioning while mitochondria are in a natural state. In this review, we summarize the use of MCA to study OXPHOS in cancer using permeabilized cells and tissues. In addition, we give examples of how this data fits into cancer research with a completely different approach and methodology.

Metabolic control analysis as a strategy to identify therapeutic targets, the case of cancer glycolysis

The use of kinetic modeling and metabolic control analysis (MCA) to identify possible therapeutic targets and to investigate the controlling and regulatory mechanisms in cancer glycolysis is here reviewed. The glycolytic pathway has been considered a target to decrease cancer cell growth; however, its occurrence in normal cells makes it difficult to design therapeutic strategies that target this pathway in pathological cells. Notwithstanding, the over-expression of all enzymes and transporters, as well as the expression of isoenzymes with different kinetic and regulatory properties in cancer cells, suggested a different distribution of the control of glycolytic flux than that observed in normal cells. Kinetic models of glycolysis are constructed with enzyme kinetics experimental data, validated with the steady-state metabolite concentrations and glycolytic fluxes; applying MCA, permitted us to identify the steps with the highest control of glycolysis in cancer cells, but low control in normal cells. The cancer glycolysis main controlling steps under several metabolic conditions were: glucose transport, hexokinase and hexose-6-phosphate isomerase (HPI); whereas in normal cells were: the first two and phosphofructokinase-1. HPI is the best therapeutic target because it exerts high control in cancer glycolytic flux, but not in normal cells. Furthermore, kinetic modeling also contributed to identifying new feed-back and feed-forward regulatory loops in cancer cells glycolysis, and to understanding the mode of metabolic action of glycolytic inhibitors. Thus, MCA and metabolic modeling allowed to propose new strategies for inhibiting glycolysis in cancer cells.

Control analysis in the identification of key enzymes driving metabolic adaptations: Towards drug target discovery

Metabolic Control Analysis (MCA) marked a turning point in understanding the design principles of metabolic network control by establishing control coefficients as a means to quantify the degree of control that an enzyme exerts on flux or metabolite concentrations. MCA has demonstrated that control of metabolic pathways is distributed among many enzymes rather than depending on a single rate-limiting step. MCA also proved that this distribution depends not only on the stoichiometric structure of the network but also on other kinetic determinants, such as the degree of saturation of the enzyme active site, the distance to thermodynamic equilibrium, and metabolite feedback regulatory loops. Consequently, predicting the alterations that occur during metabolic adaptation in response to strong changes involving a redistribution in such control distribution can be challenging. Here, using the framework provided by MCA, we illustrate how control distribution in a metabolic pathway/network depends on enzyme kinetic determinants and to what extent the redistribution of control affects our predictions on candidate enzymes suitable as targets for small molecule inhibition in the drug discovery process. Our results uncover that kinetic determinants can lead to unexpected control distribution and outcomes that cannot be predicted solely from stoichiometric determinants. We also unveil that the inference of key enzyme-drivers of an observed metabolic adaptation can be dramatically improved using mean control coefficients and ruling out those enzyme activities that are associated with low control coefficients. As the use of constraint-based stoichiometric genome-scale metabolic models (GSMMs) becomes increasingly prevalent for identifying genes/enzymes that could be potential drug targets, we anticipate that incorporating kinetic determinants and ruling out enzymes with low control coefficients into GSMM workflows will facilitate more accurate predictions and reveal novel therapeutic targets.

Enzymatic Conversion of CO2: From Natural to Artificial Utilization

Enzymatic carbon dioxide fixation is one of the most important metabolic reactions as it allows the capture of inorganic carbon from the atmosphere and its conversion into organic biomass. However, due to the often unfavorable thermodynamics and the difficulties associated with the utilization of CO2, a gaseous substrate that is found in comparatively low concentrations in the atmosphere, such reactions remain challenging for biotechnological applications. Nature has tackled these problems by evolution of dedicated CO2-fixing enzymes, i.e., carboxylases, and embedding them in complex metabolic pathways. Biotechnology employs such carboxylating and decarboxylating enzymes for the carboxylation of aromatic and aliphatic substrates either by embedding them into more complex reaction cascades or by shifting the reaction equilibrium via reaction engineering. This review aims to provide an overview of natural CO2-fixing enzymes and their mechanistic similarities. We also discuss biocatalytic applications of carboxylases and decarboxylases for the synthesis of valuable products and provide a separate summary of strategies to improve the efficiency of such processes. We briefly summarize natural CO2 fixation pathways, provide a roadmap for the design and implementation of artificial carbon fixation pathways, and highlight examples of biocatalytic cascades involving carboxylases. Additionally, we suggest that biochemical utilization of reduced CO2 derivates, such as formate or methanol, represents a suitable alternative to direct use of CO2 and provide several examples. Our discussion closes with a techno-economic perspective on enzymatic CO2 fixation and its potential to reduce CO2 emissions.

Estimation of energy pathway fluxes in cancer cells - Beyond the Warburg effect

Glycolytic and respiratory fluxes were analyzed in cancer and non-cancer cells. The steady-state fluxes in energy metabolism were used to estimate the contributions of aerobic glycolytic and oxidative phosphorylation (OxPhos) pathways to the cellular ATP supply. The rate of lactate production - corrected for the fraction generated by glutaminolysis - is proposed as the appropriate way to estimate glycolytic flux. In general, the glycolytic rates estimated for cancer cells are higher than those found in non-cancer cells, as originally observed by Otto Warburg. The rate of basal or endogenous cellular O₂ consumption corrected for non-ATP synthesizing O₂ consumption, measured after inhibition by oligomycin (a specific, potent and permeable ATP synthase inhibitor), has been proposed as the appropriate way to estimate mitochondrial ATP synthesis-linked O₂ flux or net OxPhos flux in living cells. Detecting non-negligible oligomycin-sensitive O₂ consumption rates in cancer cells has revealed that the mitochondrial function is not impaired, as claimed by the Warburg effect. Furthermore, when calculating the relative contributions to cellular ATP supply, under a variety of environmental conditions and for different types of cancer cells, it was found that OxPhos pathway was the main ATP provider over glycolysis. Hence, OxPhos pathway targeting can be successfully used to block in cancer cells ATP-dependent processes such as migration. These observations may guide the re-design of novel targeted therapies.

Comparative study of two Saccharomyces cerevisiae strains with kinetic models at genome-scale

The parameterization of kinetic models requires measurement of fluxes and/or metabolite levels for a base strain and a few genetic perturbations thereof. Unlike stoichiometric models that are mostly invariant to the specific strain, it remains unclear whether kinetic models constructed for different strains of the same species have similar or significantly different kinetic parameters. This important question underpins the applicability range and prediction limits of kinetic reconstructions. To this end, herein we parameterize two separate large-scale kinetic models using K-FIT with genome-wide coverage corresponding to two distinct strains of Saccharomyces cerevisiae: CEN.PK 113-7D strain (model k-sacce306-CENPK), and growth-deficient BY4741 (isogenic to S288c; model k-sacce306-BY4741). The metabolic network for each model contains 306 reactions, 230 metabolites, and 119 substrate-level regulatory interactions. The two models (for CEN.PK and BY4741) recapitulate, within one standard deviation, 77% and 75% of the fitted dataset fluxes, respectively, determined by ¹³C metabolic flux analysis for wild-type and eight single-gene knockout mutants of each strain. Strain-specific kinetic parameterization results indicate that key enzymes in the TCA cycle, glycolysis, and arginine and proline metabolism drive the metabolic differences between these two strains of S. cerevisiae. Our results suggest that although kinetic models cannot be readily used across strains as stoichiometric models, they can capture species-specific information through the kinetic parameterization process.

Kinetic-model-based pathway optimization with application to reverse glycolysis in mammalian cells

Over the last two decades, model-based metabolic pathway optimization tools have been developed for the design of microorganisms to produce desired metabolites. However, few have considered more complex cellular systems such as mammalian cells, which requires the use of nonlinear kinetic models to capture the effects of concentration changes and cross-regulatory interactions. In this study, we develop a new two-stage pathway optimization framework based on kinetic models that incorporate detailed kinetics and regulation information. In Stage 1, a set of optimization problems are solved to identify and rank the enzymes that contribute the most to achieving the metabolic objective. Stage 2 then determines the optimal enzyme interventions for specified desired numbers of enzyme adjustments. It also incorporates multi-scenario optimization, which allows the simultaneous consideration of multiple physiological conditions. We apply the proposed framework to find enzyme adjustments that enable a reverse glucose flow in cultured mammalian cells, thereby eliminating the need for glucose feed in the late culture stage and enhancing process robustness. The computational results demonstrate the efficacy of the proposed approach; it not only captures the important regulations and key enzymes for reverse glycolysis but also identifies differences and commonalities in the metabolic requirements for different carbon sources.

17-β Estradiol up-regulates energy metabolic pathways, cellular proliferation and tumor invasiveness in ER+ breast cancer spheroids

Several biological processes related to cancer malignancy are regulated by 17-β estradiol (E2) in ER+-breast cancer. To establish the role of E2 on the atypical cancer energy metabolism, a systematic study analyzing transcription factors, proteins, and fluxes associated with energy metabolism was undertaken in multicellular tumor spheroids (MCTS) from human ER+ MCF-7 breast cancer cells. At E2 physiological concentrations (10 and 100 nM for 24 h), both ERα and ERβ receptors, and their protein target pS2, increased by 0.6-3.5 times vs. non-treated MCTS, revealing an activated E2/ER axis. E2 also increased by 30-470% the content of several transcription factors associated to mitochondrial biogenesis and oxidative phosphorylation (OxPhos) (p53, PGC1-α) and glycolytic pathways (HIF1-α, c-MYC). Several OxPhos and glycolytic proteins (36-257%) as well as pathway fluxes (48-156%) significantly increased being OxPhos the principal ATP cellular supplier (>75%). As result of energy metabolism stimulation by E2, cancer cell migration and invasion processes and related proteins (SNAIL, FN, MM-9) contents augmented by 24-189% vs. non-treated MCTS. Celecoxib at 10 nM blocked OxPhos (60%) as well as MCTS growth, cell migration and invasiveness (>40%); whereas the glycolytic inhibitor iodoacetate (0.5 µM) and doxorubicin (70 nM) were innocuous. Our results show for the first time using a more physiological tridimensional cancer model, resembling the initial stages of solid tumors, that anti-mitochondrial therapy may be useful to deter hormone-dependent breast carcinomas.

Quantitative metabolic fluxes regulated by trans-omic networks

Cells change their metabolism in response to internal and external conditions by regulating the trans-omic network, which is a global biochemical network with multiple omic layers. Metabolic flux is a direct measure of the activity of a metabolic reaction that provides valuable information for understanding complex trans-omic networks. Over the past decades, techniques to determine metabolic fluxes, including 13C-metabolic flux analysis (13C-MFA), flux balance analysis (FBA), and kinetic modeling, have been developed. Recent studies that acquire quantitative metabolic flux and multi-omic data have greatly advanced the quantitative understanding and prediction of metabolism-centric trans-omic networks. In this review, we present an overview of 13C-MFA, FBA, and kinetic modeling as the main techniques to determine quantitative metabolic fluxes, and discuss their advantages and disadvantages. We also introduce case studies with the aim of understanding complex metabolism-centric trans-omic networks based on the determination of metabolic fluxes.

Highly Valuable Polyunsaturated Fatty Acids from Microalgae: Strategies to Improve Their Yields and Their Potential Exploitation in Aquaculture

Microalgae have a great potential for the production of healthy food and feed supplements. Their ability to convert carbon into high-value compounds and to be cultured in large scale without interfering with crop cultivation makes these photosynthetic microorganisms promising for the sustainable production of lipids. In particular, microalgae represent an alternative source of polyunsaturated fatty acids (PUFAs), whose consumption is related to various health benefits for humans and animals. In recent years, several strategies to improve PUFAs' production in microalgae have been investigated. Such strategies include selecting the best performing species and strains and the optimization of culturing conditions, with special emphasis on the different cultivation systems and the effect of different abiotic factors on PUFAs' accumulation in microalgae. Moreover, developments and results obtained through the most modern genetic and metabolic engineering techniques are described, focusing on the strategies that lead to an increased lipid production or an altered PUFAs' profile. Additionally, we provide an overview of biotechnological applications of PUFAs derived from microalgae as safe and sustainable organisms, such as aquafeed and food ingredients, and of the main techniques (and their related issues) for PUFAs' extraction and purification from microalgal biomass.

Protein acetylation effects on enzyme activity and metabolic pathway fluxes

Acetylation of proteins seems a widespread process found in the three domains of life. Several studies have shown that besides histones, acetylation of lysine residues also occurs in non-nuclear proteins. Hence, it has been suggested that this covalent modification is a mechanism that might regulate diverse metabolic pathways by modulating enzyme activity, stability, and/or subcellular localization or interaction with other proteins. However, protein acetylation levels seem to have low correlation with modification of enzyme activity and pathway fluxes. In addition, the results obtained with mutant enzymes that presumably mimic acetylation have frequently been over-interpreted. Moreover, there is a generalized lack of rigorous enzyme kinetic analysis in parallel to acetylation level determinations. The purpose of this review is to analyze the current findings on the impact of acetylation on metabolic enzymes and its repercussion on metabolic pathways function/regulation.

Regulatory role of acetylation on enzyme activity and fluxes of energy metabolism pathways

BACKGROUND

Most of the enzymes involved in the central carbon metabolism are acetylated in Lys residues. It has been claimed that this covalent modification represents a novel regulatory mechanism by which both enzyme/transporter activities and pathway fluxes can be modulated.

METHODS

To establish which enzymes are regulated by acetylation, a systematic experimental analysis of activities and acetylation profile for several energy metabolism enzymes and pathway fluxes was undertaken in cells and mitochondria.

RESULTS

The majority of the glycolytic and neighbor enzymes as well as mitochondrial enzymes indeed showed Lys-acetylation, with GLUT1, HPI, CS, ATP synthase displaying comparatively lower acetylation patterns. The incubation of cytosolic and mitochondrial fractions with recombinant Sirt-3 produced lower acetylation signals, whereas incubation with acetyl-CoA promoted protein acetylation. Significant changes in acetylation levels of MDH and IDH-2 from rat liver mitochondria revealed no change in their activities. Similar observations were attained for the cytosolic enzymes from AS-30D and HeLa cells. A minor but significant (23%) increase in the AAT-MDH complex activity induced by acetylation was observed. To examine this question further, AS-30D and HeLa cells were treated with nicotinamide and valproic acid. These compounds promoted changes in the acetylation patterns of glycolytic proteins, although their activities and the glycolytic flux (as well as the OxPhos flux) revealed no clear correlation with acetylation.

CONCLUSION

Acetylation seems to play no predominant role in the control of energy metabolism enzyme activities and pathway fluxes.

GENERAL SIGNIFICANCE

The physiological function of protein acetylation on energy metabolism pathways remains to be elucidated.

Intelligent host engineering for metabolic flux optimisation in biotechnology

Optimising the function of a protein of length N amino acids by directed evolution involves navigating a 'search space' of possible sequences of some 20N. Optimising the expression levels of P proteins that materially affect host performance, each of which might also take 20 (logarithmically spaced) values, implies a similar search space of 20P. In this combinatorial sense, then, the problems of directed protein evolution and of host engineering are broadly equivalent. In practice, however, they have different means for avoiding the inevitable difficulties of implementation. The spare capacity exhibited in metabolic networks implies that host engineering may admit substantial increases in flux to targets of interest. Thus, we rehearse the relevant issues for those wishing to understand and exploit those modern genome-wide host engineering tools and thinking that have been designed and developed to optimise fluxes towards desirable products in biotechnological processes, with a focus on microbial systems. The aim throughput is 'making such biology predictable'. Strategies have been aimed at both transcription and translation, especially for regulatory processes that can affect multiple targets. However, because there is a limit on how much protein a cell can produce, increasing kcat in selected targets may be a better strategy than increasing protein expression levels for optimal host engineering.

Model-guided dynamic control of essential metabolic nodes boosts acetyl-coenzyme A-dependent bioproduction in rewired Pseudomonas putida

Pseudomonas putida is evolutionarily endowed with features relevant for bioproduction, especially under harsh operating conditions. The rich metabolic versatility of this species, however, comes at the price of limited formation of acetyl-coenzyme A (CoA) from sugar substrates. Since acetyl-CoA is a key metabolic precursor for a number of added-value products, in this work we deployed an in silico-guided rewiring program of central carbon metabolism for upgrading P. putida as a host for acetyl-CoA-dependent bioproduction. An updated kinetic model, integrating fluxomics and metabolomics datasets in addition to manually-curated information of enzyme mechanisms, identified targets that would lead to increased acetyl-CoA levels. Based on these predictions, a set of plasmids based on clustered regularly interspaced short palindromic repeats (CRISPR) and dead CRISPR-associated protein 9 (dCas9) was constructed to silence genes by CRISPR interference (CRISPRi). Dynamic reduction of gene expression of two key targets (gltA, encoding citrate synthase, and the essential accA gene, encoding subunit A of the acetyl-CoA carboxylase complex) mediated an 8-fold increase in the acetyl-CoA content of rewired P. putida. Poly(3-hydroxybutyrate) (PHB) was adopted as a proxy of acetyl-CoA availability, and two synthetic pathways were engineered for biopolymer accumulation. By including cell morphology as an extra target for the CRISPRi approach, fully rewired P. putida strains programmed for PHB accumulation had a 5-fold increase in PHB titers in bioreactor cultures using glucose. Thus, the strategy described herein allowed for rationally redirecting metabolic fluxes in P. putida from central metabolism towards product biosynthesis-especially relevant when deletion of essential pathways is not an option.

Correlation guided Network Integration (CoNI) reveals novel genes affecting hepatic metabolism

Objective

Technological advances have brought a steady increase in the availability of various types of omics data, from genomics to metabolomics. Integrating these multi-omics data is a chance and challenge for systems biology; yet, tools to fully tap their potential remain scarce.

Methods

We present here a fully unsupervised and versatile correlation-based method – termed Correlation guided Network Integration (CoNI) – to integrate multi-omics data into a hypergraph structure that allows for the identification of effective modulators of metabolism. Our approach yields single transcripts of potential relevance that map to specific, densely connected, metabolic subgraphs or pathways.

Results

By applying our method on transcriptomics and metabolomics data from murine livers under standard Chow or high-fat diet, we identified eleven genes with potential regulatory effects on hepatic metabolism. Five candidates, including the hepatokine INHBE, were validated in human liver biopsies to correlate with diabetes-related traits such as overweight, hepatic fat content, and insulin resistance (HOMA-IR).

Conclusion

Our method's successful application to an independent omics dataset confirmed that the novel CoNI framework is a transferable, entirely data-driven, flexible, and versatile tool for multiple omics data integration and interpretation.

Application of metabolomics in clinical and laboratory gastrointestinal oncology

Metabolites are versatile bioactive molecules. They are not only the substrates and/or the products of enzymatic reactions but also act as the regulators in the systemic metabolism. Metabolomics is a high-throughput analytical strategy to qualify or quantify as many metabolites as possible in the metabolomes. It is an indispensable part of systems biology. The leading techniques in this field are mainly based on mass spectrometry and nuclear magnetic resonance spectroscopy. The metabolomic analysis has gained wide use in bioscience fields. In the tumor research arena, metabolomics can be employed to identify biomarkers for prediction, diagnosis, and prognosis. Chemotherapeutic effect evaluation and personalized medicine decision-making can also benefit from metabolomic analysis of patient biofluid or biopsy samples. Many cell-level studies can help in disease exploration. In this review, the basic features and principles of varied metabolomic analysis are introduced. The value of metabolomics in clinical and laboratory gastrointestinal cancer studies is discussed, especially for mass spectrometry applications. Besides, combined use of metabolomics and other tools to solve problems in cancer practice is briefly illustrated. In summary, metabolomics paves a new way to explore cancerous diseases in the light of small molecules.

Integrative Modeling of Gene Expression and Metabolic Networks of Arabidopsis Embryos for Identification of Seed Oil Causal Genes

Fatty acids in crop seeds are a major source for both vegetable oils and industrial applications. Genetic improvement of fatty acid composition and oil content is critical to meet the current and future demands of plant-based renewable seed oils. Addressing this challenge can be approached by network modeling to capture key contributors of seed metabolism and to identify underpinning genetic targets for engineering the traits associated with seed oil composition and content. Here, we present a dynamic model, using an Ordinary Differential Equations model and integrated time-course gene expression data, to describe metabolic networks during Arabidopsis thaliana seed development. Through in silico perturbation of genes, targets were predicted in seed oil traits. Validation and supporting evidence were obtained for several of these predictions using published reports in the scientific literature. Furthermore, we investigated two predicted targets using omics datasets for both gene expression and metabolites from the seed embryo, and demonstrated the applicability of this network-based model. This work highlights that integration of dynamic gene expression atlases generates informative models which can be explored to dissect metabolic pathways and lead to the identification of causal genes associated with seed oil traits.

Control and regulation of acetate overflow in Escherichia coli

Overflow metabolism refers to the production of seemingly wasteful by-products by cells during growth on glucose even when oxygen is abundant. Two theories have been proposed to explain acetate overflow in Escherichia coli – global control of the central metabolism and local control of the acetate pathway – but neither accounts for all observations. Here, we develop a kinetic model of E. coli metabolism that quantitatively accounts for observed behaviours and successfully predicts the response of E. coli to new perturbations. We reconcile these theories and clarify the origin, control, and regulation of the acetate flux. We also find that, in turns, acetate regulates glucose metabolism by coordinating the expression of glycolytic and TCA genes. Acetate should not be considered a wasteful end-product since it is also a co-substrate and a global regulator of glucose metabolism in E. coli. This has broad implications for our understanding of overflow metabolism.

Developing a Yeast Platform Strain for an Enhanced Taxadiene Biosynthesis by CRISPR/Cas9

Paclitaxel is an important diterpenoid commonly used as an anticancer drug. Although the paclitaxel biosynthetic pathway has been mostly revealed, some steps remain to be elucidated. The difficulties in plant transformations and the scarcity of the precursor of paclitaxel, (+)-taxa-4(5), 11(12)-diene (taxadiene), have hindered the full comprehension of paclitaxel biochemistry and, therefore, its production by biotechnological approaches. One solution is to use the budding yeast, Saccharomyces cerevisiae, as a platform to elucidate the paclitaxel biosynthesis. As taxadiene is a diterpenoid, its common precursor, geranylgeranyl pyrophosphate (GGPP), needs to be increased in yeast. In this study, we screened various GGPP synthases (GGPPS) to find the most suitable GGPPS for taxadiene production in yeast. We also optimized the taxadiene production by increasing the flux toward the terpenoid pathway. Finally, to remove selection markers, we integrated the required genes using a CRISPR/Cas9 system in the yeast genome. Our result showed that a titer of 2.02 ± 0.40 mg/L (plasmid) and 0.41 ± 0.06 mg/L (integrated) can be achieved using these strategies. This platform strain can be used to readily test the gene candidates for microbial paclitaxel biosynthesis in the future.

Role of Hyaluronic Acids and Potential as Regenerative Biomaterials in Wound Healing

The skin can protect the body from external harm, sense environmental changes, and maintain physiological homeostasis. Cutaneous repair and regeneration associated with surgical wounds, acute traumas, and chronic diseases are a central concern of healthcare. Patients may experience the failure of current treatments due to the complexity of the healing process; therefore, emerging strategies are needed. Hyaluronic acids (HAs, also known as hyaluronan), a glycosaminoglycan (GAG) of the extracellular matrix (ECM), play key roles in cell differentiation, proliferation, and migration throughout tissue development and regeneration. Recently, HA derivatives have been developed as regenerative biomaterials for treating skin damage and injury. In this review, the healing process, namely, hemostasis, inflammation, proliferation, and maturation, is described and the role of HAs in the healing process is discussed. This review also provides recent examples in the development of HA derivatives for wound healing.

The Expanding Computational Toolbox for Engineering Microbial Phenotypes at the Genome Scale

Microbial strains are being engineered for an increasingly diverse array of applications, from chemical production to human health. While traditional engineering disciplines are driven by predictive design tools, these tools have been difficult to build for biological design due to the complexity of biological systems and many unknowns of their quantitative behavior. However, due to many recent advances, the gap between design in biology and other engineering fields is closing. In this work, we discuss promising areas of development of computational tools for engineering microbial strains. We define five frontiers of active research: (1) Constraint-based modeling and metabolic network reconstruction, (2) Kinetics and thermodynamic modeling, (3) Protein structure analysis, (4) Genome sequence analysis, and (5) Regulatory network analysis. Experimental and machine learning drivers have enabled these methods to improve by leaps and bounds in both scope and accuracy. Modern strain design projects will require these tools to be comprehensively applied to the entire cell and efficiently integrated within a single workflow. We expect that these frontiers, enabled by the ongoing revolution of big data science, will drive forward more advanced and powerful strain engineering strategies.

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.

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.

A combined experimental and modelling approach for the Weimberg pathway optimisation

The oxidative Weimberg pathway for the five-step pentose degradation to α-ketoglutarate is a key route for sustainable bioconversion of lignocellulosic biomass to added-value products and biofuels. The oxidative pathway from Caulobacter crescentus has been employed in in-vivo metabolic engineering with intact cells and in in-vitro enzyme cascades. The performance of such engineering approaches is often hampered by systems complexity, caused by non-linear kinetics and allosteric regulatory mechanisms. Here we report an iterative approach to construct and validate a quantitative model for the Weimberg pathway. Two sensitive points in pathway performance have been identified as follows: (1) product inhibition of the dehydrogenases (particularly in the absence of an efficient NAD⁺ recycling mechanism) and (2) balancing the activities of the dehydratases. The resulting model is utilized to design enzyme cascades for optimized conversion and to analyse pathway performance in C. cresensus cell-free extracts.

Lactate production is a prioritized feature of adipocyte metabolism

Adipose tissue is essential for whole-body glucose homeostasis, with a primary role in lipid storage. It has been previously observed that lactate production is also an important metabolic feature of adipocytes, but its relationship to adipose and whole-body glucose disposal remains unclear. Therefore, using a combination of metabolic labeling techniques, here we closely examined lactate production of cultured and primary mammalian adipocytes. Insulin treatment increased glucose uptake and conversion to lactate, with the latter responding more to insulin than did other metabolic fates of glucose. However, lactate production did not just serve as a mechanism to dispose of excess glucose, because we also observed that lactate production in adipocytes did not solely depend on glucose availability and even occurred independently of glucose metabolism. This suggests that lactate production is prioritized in adipocytes. Furthermore, knocking down lactate dehydrogenase specifically in the fat body of Drosophila flies lowered circulating lactate and improved whole-body glucose disposal. These results emphasize that lactate production is an additional metabolic role of adipose tissue beyond lipid storage and release.

Metabolic Control Analysis for Drug Target Prioritization in Trypanosomatids

To validate therapeutic targets in metabolic pathways of trypanosomatids, the criterion of enzyme essentiality determined by gene knockout or knockdown is usually being applied. Since, it is often found that most of the enzymes/proteins analyzed are essential, additional criteria have to be implemented for drug target prioritization. Metabolic control analysis (MCA), often in conjunction with kinetic pathway modeling, offers such possibility for prioritization. MCA is a theoretical and experimental approach to analyze how metabolic pathways are controlled. It involves strategies to perform quantitative analyses to determine the degree in which an enzyme controls a pathway flux, a value called flux control coefficient ([Formula: see text]). By determining the [Formula: see text] of individual steps in a metabolic pathway, the distribution of control of the pathway is established, that is, the identification of the main flux-controlling steps. Therefore, MCA can help in ranking pathway enzymes as drug targets from a metabolic perspective. In this chapter, three approaches to determine [Formula: see text] are reviewed: (1) In vitro pathway reconstitution, (2) manipulation of enzyme activities within parasites, and (3) in silico kinetic modeling of the metabolic pathway. To perform these methods, accurate experimental data of enzyme activities, metabolite concentrations and pathway fluxes are necessary. The methodology is illustrated with the example of trypanothione metabolism of Trypanosoma cruzi and protocols to determine such experimental data for this metabolic process are also described. However, the MCA strategy can be applied to any metabolic pathway in the parasite and general directions to perform it are provided in this chapter.

Every-Other-Day Feeding Decreases Glycolytic and Mitochondrial Energy-Producing Potentials in the Brain and Liver of Young Mice

Intermittent fasting is used to reduce body mass in obese adult humans and animals. However, information on the impact of one type of intermittent fasting (IF) called every-other-day feeding (EODF) on young animals is scarce. In this study, 1-month-old mice of both sexes were subjected to a 4-week regimen of EODF using age-matched counterparts fed ad libitum as controls. At the end of EODF exposure, experimental male and female mice weighed 14 and 13% less than the control counterparts. The EODF regimen resulted in lower liver levels of glycogen, glucose, and lactate, but did not affect lactate level in mouse cerebral cortex of both sexes. Activities of key glycolytic enzymes (hexokinase, phosphofructokinase, and pyruvate kinase) in liver of experimental mice were lower than those in controls. In the cerebral cortex, only hexokinase and pyruvate kinase activities were lower than in controls, but phosphofructokinase activity was not affected in IF females and was higher in IF males as compared with ad libitum fed males. Mitochondria isolated from liver of IF mice had lower respiratory control ratios, but those from the cortex had the same values as control animals. The concentration of β-hydroxybutyrate and the activity of β-hydroxybutyrate dehydrogenase were lower in the IF mouse liver, but not changed or enhanced in the IF cerebral cortex. Thus, animal responses to IF do not depend significantly on sex and are directed to decrease energy metabolism to save resources, and the effects are more pronounced in the liver than in the brain.

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.

Engineering microbial chemical factories using metabolic models

Living organisms in analogy with chemical factories use simple molecules such as sugars to produce a variety of compounds which are necessary for sustaining life and some of which are also commercially valuable. The metabolisms of simple (such as bacteria) and higher organisms (such as plants) alike can be exploited to convert low value inputs into high value outputs. Unlike conventional chemical factories, microbial production chassis are not necessarily tuned for a single product overproduction. Despite the same end goal, metabolic and industrial engineers rely on different techniques for achieving productivity goals. Metabolic engineers cannot affect reaction rates by manipulating pressure and temperature, instead they have at their disposal a range of enzymes and transcriptional and translational processes to optimize accordingly. In this review, we first highlight how various analytical approaches used in metabolic engineering and synthetic biology are related to concepts developed in systems and control engineering. Specifically, how algorithmic concepts derived in operations research can help explain the structure and organization of metabolic networks. Finally, we consider the future directions and challenges faced by the field of metabolic network modeling and the possible contributions of concepts drawn from the classical fields of chemical and control engineering. The aim of the review is to offer a current perspective of metabolic engineering and all that it entails without requiring specialized knowledge of bioinformatics or systems biology.

Targeting pteridine reductase 1 and dihydrofolate reductase: the old is a new trend for leishmaniasis drug discovery

Leishmaniasis is one of the major neglected tropical diseases in the world and it is considered endemic in 88 countries. This disease is transmitted by a Leishmania spp. infected sandfly and it may lead to cutaneous or systemic manifestations. The preconized treatment has low efficacy and there are cases of resistance to some drugs. Therefore, the search for new efficient molecular targets that can lead to the preparation of new drugs must be pursued. This review aims to evaluate both Leishmania enzymes PTR1 and DHFR-TS as potential drug targets, highlight their inhibitors and to discuss critically the use of chemoinformatics to elucidate interactions and propose new molecules against these enzymes.

Gamma-glutamylcysteine synthetase and tryparedoxin 1 exert high control on the antioxidant system in Trypanosoma cruzi contributing to drug resistance and infectivity

Trypanothione (T(SH)₂) is the main antioxidant metabolite for peroxide reduction in Trypanosoma cruzi; therefore, its metabolism has attracted attention for therapeutic intervention against Chagas disease. To validate drug targets within the T(SH)₂ metabolism, the strategies and methods of Metabolic Control Analysis and kinetic modeling of the metabolic pathway were used here, to identify the steps that mainly control the pathway fluxes and which could be appropriate sites for therapeutic intervention. For that purpose, gamma-glutamylcysteine synthetase (γECS), trypanothione synthetase (TryS), trypanothione reductase (TryR) and the tryparedoxin cytosolic isoform 1 (TXN1) were separately overexpressed to different levels in T. cruzi epimastigotes and their degrees of control on the pathway flux as well as their effect on drug resistance and infectivity determined. Both experimental in vivo as well as in silico analyses indicated that γECS and TryS control T(SH)₂ synthesis by 60–74% and 15–31%, respectively. γECS overexpression prompted up to a 3.5-fold increase in T(SH)₂ concentration, whereas TryS overexpression did not render an increase in T(SH)₂ levels as a consequence of high T(SH)₂ degradation. The peroxide reduction flux was controlled for 64–73% by TXN1, 17–20% by TXNPx and 11–16% by TryR. TXN1 and TryR overexpression increased H₂O₂ resistance, whereas TXN1 overexpression increased resistance to the benznidazole plus buthionine sulfoximine combination. γECS overexpression led to an increase in infectivity capacity whereas that of TXN increased trypomastigote bursting. The present data suggested that inhibition of high controlling enzymes such as γECS and TXN1 in the T(SH)₂ antioxidant pathway may compromise the parasite's viability and infectivity.

•

The trypanothione synthesis flux is primarily but not exclusively controlled by γECS.

•

Tryparedoxin exerts high control on the peroxide reduction flux.

•

Kinetic metabolic modeling may reliably predict the in vivo pathway behavior.

•

TXN1 overexpression provides benznidazole resistance.

•

γECS and TXN contribute to parasite infectivity.

Computational modelling of energy balance in individuals with Metabolic Syndrome

Background

A positive energy balance is considered to be the primary cause of the development of obesity-related diseases. Treatment often consists of a combination of reducing energy intake and increasing energy expenditure. Here we use an existing computational modelling framework describing the long-term development of Metabolic Syndrome (MetS) in APOE3L.CETP mice fed a high-fat diet containing cholesterol with a human-like metabolic system. This model was used to analyze energy expenditure and energy balance in a large set of individual model realizations.

Results

We developed and applied a strategy to select specific individual models for a detailed analysis of heterogeneity in energy metabolism. Models were stratified based on energy expenditure. A substantial surplus of energy was found to be present during MetS development, which explains the weight gain during MetS development. In the majority of the models, energy was mainly expended in the peripheral tissues, but also distinctly different subgroups were identified.

In silico perturbation of the system to induce increased peripheral energy expenditure implied changes in lipid metabolism, but not in carbohydrate metabolism. In silico analysis provided predictions for which individual models increase of peripheral energy expenditure would be an effective treatment.

Conclusion

The computational analysis confirmed that the energy imbalance plays an important role in the development of obesity. Furthermore, the model is capable to predict whether an increase in peripheral energy expenditure – for instance by cold exposure to activate brown adipose tissue (BAT) – could resolve MetS symptoms.

Electronic supplementary material

The online version of this article (10.1186/s12918-019-0705-z) contains supplementary material, which is available to authorized users.

Control and regulation of the pyrophosphate-dependent glucose metabolism in Entamoeba histolytica

Entamoeba histolytica has neither Krebs cycle nor oxidative phosphorylation activities; therefore, glycolysis is the main pathway for ATP supply and provision of carbon skeleton precursors for the synthesis of macromolecules. Glucose is metabolized through fermentative glycolysis, producing ethanol as its main end-product as well as some acetate. Amoebal glycolysis markedly differs from the typical Embden-Meyerhof-Parnas pathway present in human cells: (i) by the use of inorganic pyrophosphate, instead of ATP, as the high-energy phospho group donor; (ii) with one exception, the pathway enzymes can catalyze reversible reactions under physiological conditions; (iii) there is no allosteric regulation and sigmoidal kinetic behavior of key enzymes; and (iv) the presence of some glycolytic and fermentation enzymes similar to those of anaerobic bacteria. These peculiarities bring about alternative mechanisms of control and regulation of the PPi-dependent fermentative glycolysis in the parasite in comparison to the ATP-dependent and allosterically regulated glycolysis in many other eukaryotic cells. In this review, the current knowledge of the carbohydrate metabolism enzymes in E. histolytica is analyzed. Thermodynamics and stoichiometric analyses indicate 2 to 3.5 ATP yield per glucose metabolized, instead of the often presumed 5 ATP/glucose ratio. PPi derived from anabolism seems insufficient for PPi-glycolysis; hence, alternative ways of PPi supply are also discussed. Furthermore, the underlying mechanisms of control and regulation of the E. histolytica carbohydrate metabolism, analyzed by applying integral and systemic approaches such as Metabolic Control Analysis and kinetic modeling, contribute to unveiling alternative and promising drug targets.

Critical comparison of methods for fault diagnosis in metabolomics data

Platforms like metabolomics provide an unprecedented view on the chemical versatility in biomedical samples. Many diseases reflect themselves as perturbations in specific metabolite combinations. Multivariate analyses are essential to detect such combinations and associate them to specific diseases. For this, usually targeted discriminations of samples associated to a specific disease from non-diseased control samples are used. Such targeted data interpretation may not respect the heterogeneity of metabolic responses, both between diseases and within diseases. Here we show that multivariate methods that find any set of perturbed metabolites in a single patient, may be employed in combination with data collected with a single metabolomics technology to simultaneously investigate a large array of diseases. Several such untargeted data analysis approaches have been already proposed in other fields to find both expected and unexpected perturbations, e.g. in Statistical Process Control. We have critically compared several of these approaches for their sensitivity and their correct identification of the specifically perturbed metabolites. Also a new approach is introduced for this purpose. The newly introduced Sparse Mean approach, which we find here as most sensitive and best able to identify the specifically perturbed metabolites, turns metabolomics into an untargeted diagnostic platform. Aside from metabolomics, the proposed approach may greatly benefit fault diagnosis with untargeted analyses in many other fields, such as Industrial Process Control, food Adulteration Detection, and Intrusion Detection.

Thermodynamic constraints on the regulation of metabolic fluxes

Nutrition and metabolism are fundamental to cellular function. Metabolic activity (i.e. rates of flow, most commonly referred to as flux) is constrained by thermodynamics and regulated by the activity of enzymes. The general principles that relate biological and physical variables to metabolic control are incompletely understood. Using metabolic control analysis and computer simulations in several models of simplified metabolic pathways, we derive analytical expressions that define relationships between thermodynamics, enzyme activity, and flux control. The relationships are further analyzed in a mathematical model of glycolysis as an example of a complex biochemical pathway. We show that metabolic pathways that are very far from equilibrium are controlled by the activity of upstream enzymes. However, in general, regulation of metabolic fluxes by an enzyme has a more adaptable pattern, which relies more on distribution of free energy among reaction steps in the pathway than on the thermodynamic properties of the given enzyme. These findings show how the control of metabolic pathways is shaped by thermodynamic constraints of the given pathway.

Delving deeper: Relating the behaviour of a metabolic system to the properties of its components using symbolic metabolic control analysis

High-level behaviour of metabolic systems results from the properties of, and interactions between, numerous molecular components. Reaching a complete understanding of metabolic behaviour based on the system’s components is therefore a difficult task. This problem can be tackled by constructing and subsequently analysing kinetic models of metabolic pathways since such models aim to capture all the relevant properties of the system components and their interactions. Symbolic control analysis is a framework for analysing pathway models in order to reach a mechanistic understanding of their behaviour. By providing algebraic expressions for the sensitivities of system properties, such as metabolic flux or steady-state concentrations, in terms of the properties of individual reactions it allows one to trace the high level behaviour back to these low level components. Here we apply this method to a model of pyruvate branch metabolism in Lactococcus lactis in order to explain a previously observed negative flux response towards an increase in substrate concentration. With this method we are able to show, first, that the sensitivity of flux towards changes in reaction rates (represented by flux control coefficients) is determined by the individual metabolic branches of the pathway, and second, how the sensitivities of individual reaction rates towards their substrates (represented by elasticity coefficients) contribute to this flux control. We also quantify the contributions of enzyme binding and mass-action to enzyme elasticity separately, which allows for an even finer-grained understanding of flux control. These analytical tools allow us to analyse the control properties of a metabolic model and to arrive at a mechanistic understanding of the quantitative contributions of each of the enzymes to this control. Our analysis provides an example of the descriptive power of the general principles of symbolic control analysis.

Noise propagation in an integrated model of bacterial gene expression and growth

In bacterial cells, gene expression, metabolism, and growth are highly interdependent and tightly coordinated. As a result, stochastic fluctuations in expression levels and instantaneous growth rate show intricate cross-correlations. These correlations are shaped by feedback loops, trade-offs and constraints acting at the cellular level; therefore a quantitative understanding requires an integrated approach. To that end, we here present a mathematical model describing a cell that contains multiple proteins that are each expressed stochastically and jointly limit the growth rate. Conversely, metabolism and growth affect protein synthesis and dilution. Thus, expression noise originating in one gene propagates to metabolism, growth, and the expression of all other genes. Nevertheless, under a small-noise approximation many statistical quantities can be calculated analytically. We identify several routes of noise propagation, illustrate their origins and scaling, and establish important connections between noise propagation and the field of metabolic control analysis. We then present a many-protein model containing >1000 proteins parameterized by previously measured abundance data and demonstrate that the predicted cross-correlations between gene expression and growth rate are in broad agreement with published measurements.