Construction and validation of a detailed kinetic model of glycolysis in Plasmodium falciparum

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.

A detailed kinetic model of glycolysis in Plasmodium falciparum-infected red blood cells for antimalarial drug target identification

Upon infection by the malaria parasite Plasmodium falciparum, the glycolytic rate of a red blood cell increases up to 100-fold, possibly contributing to lactic acidosis and hypoglycemia in patients with severe malaria. This dramatic increase in glucose uptake and metabolism was correctly predicted by a newly constructed detailed enzyme kinetic model of glucose metabolism in the trophozoite-infected red blood cell. Subsequently, we expanded the model to simulate an infected red blood cell culture, including the different asexual blood-stage forms of the malaria parasite. The model simulations were in good agreement with experimental data, for which the measured parasitic volume was an important parameter. Upon further analysis of the model, we identified glucose transport as a drug target that would specifically affect infected red blood cells, which was confirmed experimentally with inhibitor titrations. This model can be a first step in constructing a whole-body model for glucose metabolism in malaria patients to evaluate the contribution of the parasite's metabolism to the disease state.

Gear Shifting in Biological Energy Transduction

Confronted with thermodynamically adverse output processes, free-energy transducers may shift to lower gears, thereby reducing output per unit input. This option is well known for inanimate machines such as automobiles, but unappreciated in biology. The present study extends existing non-equilibrium thermodynamic principles to underpin biological gear shifting and identify possible mechanisms. It shows that gear shifting differs from altering the degree of coupling and that living systems may use it to optimize their performance: microbial growth is ultimately powered by the Gibbs energy of catabolism, which is partially transformed into Gibbs energy ('output force') in the ATP that is produced. If this output force is high, the cell may turn to a catabolic pathway with a lower ATP stoichiometry. Notwithstanding the reduced stoichiometry, the ATP synthesis flux may then actually increase as compared to that in a system without gear shift, in which growth might come to a halt. A 'variomatic' gear switching strategy should be optimal, explaining why organisms avail themselves of multiple catabolic pathways, as these enable them to shift gears when the growing gets tough.

Identification of antimalarial targets of chloroquine by a combined deconvolution strategy of ABPP and MS-CETSA

BACKGROUND

Malaria is a devastating infectious disease that disproportionally threatens hundreds of millions of people in developing countries. In the history of anti-malaria campaign, chloroquine (CQ) has played an indispensable role, however, its mechanism of action (MoA) is not fully understood.

METHODS

We used the principle of photo-affinity labeling and click chemistry-based functionalization in the design of a CQ probe and developed a combined deconvolution strategy of activity-based protein profiling (ABPP) and mass spectrometry-coupled cellular thermal shift assay (MS-CETSA) that identified the protein targets of CQ in an unbiased manner in this study. The interactions between CQ and these identified potential protein hits were confirmed by biophysical and enzymatic assays.

RESULTS

We developed a novel clickable, photo-affinity chloroquine analog probe (CQP) which retains the antimalarial activity in the nanomole range, and identified a total of 40 proteins that specifically interacted and photo-crosslinked with CQP which was inhibited in the presence of excess CQ. Using MS-CETSA, we identified 83 candidate interacting proteins out of a total of 3375 measured parasite proteins. At the same time, we identified 8 proteins as the most potential hits which were commonly identified by both methods.

CONCLUSIONS

We found that CQ could disrupt glycolysis and energy metabolism of malarial parasites through direct binding with some of the key enzymes, a new mechanism that is different from its well-known inhibitory effect of hemozoin formation. This is the first report of identifying CQ antimalarial targets by a parallel usage of labeled (ABPP) and label-free (MS-CETSA) methods.

All Driven by Energy Demand? Integrative Comparison of Metabolism of Enterococcus faecalis Wildtype and a Glutamine Synthase Mutant

Lactic acid bacteria (LAB) play a significant role in biotechnology, e.g., food industry and also in human health. Many LAB genera have developed a multidrug resistance in the past few years, causing a serious problem in controlling hospital germs worldwide. Enterococcus faecalis accounts for a large part of the human infections caused by LABs. Therefore, studying its adaptive metabolism under various environmental conditions is particularly important to promote the development of new therapeutic approaches. In this study, we investigated the effect of glutamine auxotrophy (ΔglnA mutant) on metabolic and proteomic adaptations of E. faecalis in response to a changing pH in its environment. Changing pH values are part of the organism's natural environment in the human body and play a role in the food industry. We compared the results with those of the wildtype. Using a genome-scale metabolic model constrained by metabolic and proteomic data, our integrative method allows us to understand the bigger picture of the adaptation strategies of this bacterium. The study showed that energy demand is the decisive factor in adapting to a new environmental pH. The energy demand of the mutant was higher at all conditions. It has been reported that ΔglnA mutants of bacteria are energetically less effective. With the aid of our data and model we are able to explain this phenomenon as a consequence of a failure to regulate glutamine uptake and the costs for the import of glutamine and the export of ammonium. Methodologically, it became apparent that taking into account the nonspecificity of amino acid transporters is important for reproducing metabolic changes with genome-scale models because it affects energy balance. IMPORTANCE The integration of new pH-dependent experimental data on metabolic uptake and release fluxes, as well as of proteome data with a genome-scale computational model of a glutamine synthetase mutant of E. faecalis is used and compared with those of the wildtype to understand why glutamine auxotrophy results in a less efficient metabolism and how-in comparison with the wildtype-the glutamine synthetase knockout impacts metabolic adjustments during acidification or simply exposure to lower pH. We show that forced glutamine auxotrophy causes more energy demand and that this is likely due to a disregulated glutamine uptake. Proteome changes during acidification observed for the mutant resemble those of the wildtype with the exception of glycolysis-related genes, as the mutant is already energetically stressed at a higher pH and the respective proteome changes were in effect.

Operational research: A multidisciplinary approach for the management of infectious disease in a global context

Infectious diseases, both established and emerging, impose a significant burden globally. Successful management of infectious diseases requires considerable effort and a multidisciplinary approach to tackle the complex web of interconnected biological, public health and economic systems. Through a wide range of problem-solving techniques and computational methods, operational research can strengthen health systems and support decision-making at all levels of disease control. From improved understanding of disease biology, intervention planning and implementation, assessing economic feasibility of new strategies, identifying opportunities for cost reductions in routine processes, and informing health policy, this paper highlights areas of opportunity for operational research to contribute to effective and efficient infectious disease management and improved health outcomes.

Chlamydia trachomatis glyceraldehyde 3-phosphate dehydrogenase: Enzyme kinetics, high-resolution crystal structure, and plasminogen binding

Glyceraldehyde 3-phosphate dehydrogenase (GAPDH) is an evolutionarily conserved essential enzyme in the glycolytic pathway. GAPDH is also involved in a wide spectrum of non-catalytic cellular 'moonlighting' functions. Bacterial surface-associated GAPDHs engage in many host interactions that aid in colonization, pathogenesis, and virulence. We have structurally and functionally characterized the recombinant GAPDH of the obligate intracellular bacteria Chlamydia trachomatis, the leading cause of sexually transmitted bacterial and ocular infections. Contrary to earlier speculations, recent data confirm the presence of glucose-catabolizing enzymes including GAPDH in both stages of the biphasic life cycle of the bacterium. The high-resolution crystal structure described here provides a close-up view of the enzyme's active site and surface topology and reveals two chemically modified cysteine residues. Moreover, we show for the first time that purified C. trachomatis GAPDH binds to human plasminogen and plasmin. Based on the versatility of GAPDH's functions, data presented here emphasize the need for investigating the Chlamydiae GAPDH's involvement in biological functions beyond energy metabolism.

A compiler for biological networks on silicon chips

The explosive growth in semiconductor integrated circuits was made possible in large part by design automation software. The design and/or analysis of synthetic and natural circuits in living cells could be made more scalable using the same approach. We present a compiler which converts standard representations of chemical reaction networks and circuits into hardware configurations that can be used to simulate the network on specialized cytomorphic hardware. The compiler also creates circuit–level models of the target configuration, which enhances the versatility of the compiler and enables the validation of its functionality without physical experimentation with the hardware. We show that this compiler can translate networks comprised of mass–action kinetics, classic enzyme kinetics (Michaelis–Menten, Briggs–Haldane, and Botts–Morales formalisms), and genetic repressor kinetics, thereby allowing a large class of models to be transformed into a hardware representation. Rule–based models are particularly well–suited to this approach, as we demonstrate by compiling a MAP kinase model. Development of specialized hardware and software for simulating biological networks has the potential to enable the simulation of larger kinetic models than are currently feasible or allow the parallel simulation of many smaller networks with better performance than current simulation software.

We present a “silicon compiler” that is capable of translating biochemical models encoded in the SBML standard into specialized analog cytomorphic hardware and transfer function–level simulations of such hardware. We show how the compiler and hardware address challenges in analog computing: 1) We ensure that the integration of errors due to the mismatch between analog circuit parameters does not become infinite over time but always remains finite via the use of total variables (the solution of the “divergence problem”); 2) We describe the compilation process through a series of examples using building blocks of biological networks, and show the results of compiling two SBML models from the literature: the Elowitz repressilator model and a rule–based model of a MAP kinase cascade. Source code for the compiler is available at https://doi.org/10.5281/zenodo.3948393.

Repurposing bioenergetic modulators against protozoan parasites responsible for tropical diseases

Malaria, leishmaniasis and trypanosomiasis are arthropod-borne, parasitic diseases that constitute a major global health problem. They are generally found in developing countries, where lack of access to preventive tools and treatment hinders their management. Because these parasites share an increased demand on glucose consumption with most cancer cells, six compounds used in anti-tumoral research were selected to be tested as antiparasitic agents in in vitro models of Leishmania infantum, Trypanosoma brucei, T. cruzi, and Plasmodium falciparum: dichloroacetic acid (DCA), 3-bromopyruvic acid (3BP), 2-deoxy-D-glucose (2DG), lonidamine (LND), metformin (MET), and sirolimus (SIR). No parasite-killing activity was found in L. infantum promastigotes, whereas DCA and 3BP reduced the burden of intra-macrophagic amastigotes. For T. brucei all selected compounds, but 2DG, decreased parasite survival. DCA, 2DG, LND and MET showed parasite-killing activity in T. cruzi. Finally, anti-plasmodial activity was found for DCA, 2DG, LND, MET and SIR. These results reinforce the hypothesis that drugs with proven efficacy in the treatment of cancer by interfering with ATP production, proliferation, and survival cell strategies might be useful in treating threatening parasitic diseases and provide new opportunities for their repurposing.

•

Parasitic diseases are prevalent among the poorest of the poor.

•

Some parasitic protists degrade glucose into CO₂ even aerobically making this a target.

•

Degrading glucose into CO₂ (Warburg effect) is also characteristic for cancer cells.

•

Repurposing cancer glycolysis blockers may provide cost-effective treatments for the poorest.

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.

Suppression of Drug Resistance Reveals a Genetic Mechanism of Metabolic Plasticity in Malaria Parasites

In the malaria parasite Plasmodium falciparum, synthesis of isoprenoids from glycolytic intermediates is essential for survival. The antimalarial fosmidomycin (FSM) inhibits isoprenoid synthesis. In P. falciparum, we identified a loss-of-function mutation in HAD2 (P. falciparum 3D7_1226300 [PF3D7_1226300]) as necessary for FSM resistance. Enzymatic characterization revealed that HAD2, a member of the haloacid dehalogenase-like hydrolase (HAD) superfamily, is a phosphatase. Harnessing a growth defect in resistant parasites, we selected for suppression of HAD2-mediated FSM resistance and uncovered hypomorphic suppressor mutations in the locus encoding the glycolytic enzyme phosphofructokinase 9 (PFK9). Metabolic profiling demonstrated that FSM resistance is achieved via increased steady-state levels of methylerythritol phosphate (MEP) pathway and glycolytic intermediates and confirmed reduced PFK9 function in the suppressed strains. We identified HAD2 as a novel regulator of malaria parasite metabolism and drug sensitivity and uncovered PFK9 as a novel site of genetic metabolic plasticity in the parasite. Our report informs the biological functions of an evolutionarily conserved family of metabolic regulators and reveals a previously undescribed strategy by which malaria parasites adapt to cellular metabolic dysregulation.IMPORTANCE Unique and essential aspects of parasite metabolism are excellent targets for development of new antimalarials. An improved understanding of parasite metabolism and drug resistance mechanisms is urgently needed. The antibiotic fosmidomycin targets the synthesis of essential isoprenoid compounds from glucose and is a candidate for antimalarial development. Our report identifies a novel mechanism of drug resistance and further describes a family of metabolic regulators in the parasite. Using a novel forward genetic approach, we also uncovered mutations that suppress drug resistance in the glycolytic enzyme PFK9. Thus, we identify an unexpected genetic mechanism of adaptation to metabolic insult that influences parasite fitness and tolerance of antimalarials.

Systems biology of the modified branched Entner-Doudoroff pathway in Sulfolobus solfataricus

Sulfolobus solfataricus is a thermoacidophilic Archaeon that thrives in terrestrial hot springs (solfatares) with optimal growth at 80°C and pH 2–4. It catabolizes specific carbon sources, such as D-glucose, to pyruvate via the modified Entner-Doudoroff (ED) pathway. This pathway has two parallel branches, the semi-phosphorylative and the non-phosphorylative. However, the strategy of S.solfataricus to endure in such an extreme environment in terms of robustness and adaptation is not yet completely understood. Here, we present the first dynamic mathematical model of the ED pathway parameterized with quantitative experimental data. These data consist of enzyme activities of the branched pathway at 70°C and 80°C and of metabolomics data at the same temperatures for the wild type and for a metabolic engineered knockout of the semi-phosphorylative branch. We use the validated model to address two questions: 1. Is this system more robust to perturbations at its optimal growth temperature? 2. Is the ED robust to deletion and perturbations? We employed a systems biology approach to answer these questions and to gain further knowledge on the emergent properties of this biological system. Specifically, we applied deterministic and stochastic approaches to study the sensitivity and robustness of the system, respectively. The mathematical model we present here, shows that: 1. Steady state metabolite concentrations of the ED pathway are consistently more robust to stochastic internal perturbations at 80°C than at 70°C; 2. These metabolite concentrations are highly robust when faced with the knockout of either branch. Connected with this observation, these two branches show different properties at the level of metabolite production and flux control. These new results reveal how enzyme kinetics and metabolomics synergizes with mathematical modelling to unveil new systemic properties of the ED pathway in S.solfataricus in terms of its adaptation and robustness.

FAIRDOMHub: a repository and collaboration environment for sharing systems biology research

The FAIRDOMHub is a repository for publishing FAIR (Findable, Accessible, Interoperable and Reusable) Data, Operating procedures and Models (https://fairdomhub.org/) for the Systems Biology community. It is a web-accessible repository for storing and sharing systems biology research assets. It enables researchers to organize, share and publish data, models and protocols, interlink them in the context of the systems biology investigations that produced them, and to interrogate them via API interfaces. By using the FAIRDOMHub, researchers can achieve more effective exchange with geographically distributed collaborators during projects, ensure results are sustained and preserved and generate reproducible publications that adhere to the FAIR guiding principles of data stewardship.

GLUT1-mediated glucose uptake plays a crucial role during Plasmodium hepatic infection

Intracellular pathogens have evolved mechanisms to ensure their survival and development inside their host cells. Here, we show that glucose is a pivotal modulator of hepatic infection by the rodent malaria parasite Plasmodium berghei and that glucose uptake via the GLUT1 transporter is specifically enhanced in P. berghei‐infected cells. We further show that ATP levels of cells containing developing parasites are decreased, which is known to enhance membrane GLUT1 activity. In addition, GLUT1 molecules are translocated to the membrane of the hepatic cell, increasing glucose uptake at later stages of infection. Chemical inhibition of GLUT1 activity leads to a decrease in glucose uptake and the consequent impairment of hepatic infection, both in vitro and in vivo. Our results reveal that changes in GLUT1 conformation and cellular localization seem to be part of an adaptive host response to maintain adequate cellular nutrition and energy levels, ensuring host cell survival and supporting P. berghei hepatic development.

How Modeling Standards, Software, and Initiatives Support Reproducibility in Systems Biology and Systems Medicine

OBJECTIVE

Only reproducible results are of significance to science. The lack of suitable standards and appropriate support of standards in software tools has led to numerous publications with irreproducible results. Our objectives are to identify the key challenges of reproducible research and to highlight existing solutions.

RESULTS

In this paper, we summarize problems concerning reproducibility in systems biology and systems medicine. We focus on initiatives, standards, and software tools that aim to improve the reproducibility of simulation studies.

CONCLUSIONS

The long-term success of systems biology and systems medicine depends on trustworthy models and simulations. This requires openness to ensure reusability and transparency to enable reproducibility of results in these fields.

Targeting glycolysis in the malaria parasite Plasmodium falciparum

Glycolysis is the main pathway for ATP production in the malaria parasite Plasmodium falciparum and essential for its survival. Following a sensitivity analysis of a detailed kinetic model for glycolysis in the parasite, the glucose transport reaction was identified as the step whose activity needed to be inhibited to the least extent to result in a 50% reduction in glycolytic flux. In a subsequent inhibitor titration with cytochalasin B, we confirmed the model analysis experimentally and measured a flux control coefficient of 0.3 for the glucose transporter. In addition to the glucose transporter, the glucokinase and phosphofructokinase had high flux control coefficients, while for the ATPase a small negative flux control coefficient was predicted. In a broader comparative analysis of glycolytic models, we identified a weakness in the P. falciparum pathway design with respect to stability towards perturbations in the ATP demand.

DATABASE

The mathematical model described here has been submitted to the JWS Online Cellular Systems Modelling Database and can be accessed at http://jjj.bio.vu.nl/database/vanniekerk1. The SEEK-study including the experimental data set is available at DOI 10.15490/seek.1.

INVESTIGATION

56 (http://dx.doi.org/10.15490/seek.1.

INVESTIGATION

56).

Quantitative analysis of drug effects at the whole-body level: a case study for glucose metabolism in malaria patients

We propose a hierarchical modelling approach to construct models for disease states at the whole-body level. Such models can simulate effects of drug-induced inhibition of reaction steps on the whole-body physiology. We illustrate the approach for glucose metabolism in malaria patients, by merging two detailed kinetic models for glucose metabolism in the parasite Plasmodium falciparum and the human red blood cell with a coarse-grained model for whole-body glucose metabolism. In addition we use a genome-scale metabolic model for the parasite to predict amino acid production profiles by the malaria parasite that can be used as a complex biomarker.

Cell scale host-pathogen modeling: another branch in the evolution of constraint-based methods

Constraint-based models have become popular methods for systems biology as they enable the integration of complex, disparate datasets in a biologically cohesive framework that also supports the description of biological processes in terms of basic physicochemical constraints and relationships. The scope, scale, and application of genome scale models have grown from single cell bacteria to multi-cellular interaction modeling; host-pathogen modeling represents one of these examples at the current horizon of constraint-based methods. There are now a small number of examples of host-pathogen constraint-based models in the literature, however there has not yet been a definitive description of the methodology required for the functional integration of genome scale models in order to generate simulation capable host-pathogen models. Herein we outline a systematic procedure to produce functional host-pathogen models, highlighting steps which require debugging and iterative revisions in order to successfully build a functional model. The construction of such models will enable the exploration of host-pathogen interactions by leveraging the growing wealth of omic data in order to better understand mechanism of infection and identify novel therapeutic strategies.

Rites of passage: requirements and standards for building kinetic models of metabolic phenotypes

The overarching ambition of kinetic metabolic modeling is to capture the dynamic behavior of metabolism to such an extent that systems and synthetic biology strategies can reliably be tested in silico. The lack of kinetic data hampers the development of kinetic models, and most of the current models use ad hoc reduced stoichiometry or oversimplified kinetic rate expressions, which may limit their predictive strength. There is a need to introduce the community-level standards that will organize and accelerate the future developments in this area. We introduce here a set of requirements that will ensure the model quality, we examine the current kinetic models with respect to these requirements, and we propose a general workflow for constructing models that satisfy these requirements.