Bioenergetics-based modeling of Plasmodium falciparum metabolism reveals its essential genes, nutritional requirements, and thermodynamic bottlenecks

Host cell CRISPR genomics and modelling reveal shared metabolic vulnerabilities in the intracellular development of Plasmodium falciparum and related hemoparasites

Parasitic diseases, particularly malaria (caused by Plasmodium falciparum) and theileriosis (caused by Theileria spp.), profoundly impact global health and the socioeconomic well-being of lower-income countries. Despite recent advances, identifying host metabolic proteins essential for these auxotrophic pathogens remains challenging. Here, we generate a novel metabolic model of human hepatocytes infected with P. falciparum and integrate it with a genome-wide CRISPR knockout screen targeting Theileria-infected cells to pinpoint shared vulnerabilities. We identify key host metabolic enzymes critical for the intracellular survival of both of these lethal hemoparasites. Remarkably, among the metabolic proteins identified by our synergistic approach, we find that host purine and heme biosynthetic enzymes are essential for the intracellular survival of P. falciparum and Theileria, while other host enzymes are only essential under certain metabolic conditions, highlighting P. falciparum's adaptability and ability to scavenge nutrients selectively. Unexpectedly, host porphyrins emerge as being essential for both parasites. The shared vulnerabilities open new avenues for developing more effective therapies against these debilitating diseases, with the potential for broader applicability in combating apicomplexan infections.

The glycobiology of plasmodium falciparum: New approaches and recent advances

Like any other microorganism, pathogenic protozoan parasites rely heavily on glycoconjugates and glycan binding proteins to protect themselves from the environment and to interact with their diverse hosts. A thorough comprehension of how glycobiology contributes to the survival and virulence of these organisms may reveal unknown aspects of their biology and may open much needed avenues for the design of new strategies against them. In the case of Plasmodium falciparum, which causes the vast majority of malaria cases and deaths, the restricted variety and the simplicity of its glycans seemed to confer limited significance to the role played by glycoconjugates in the parasite. Nonetheless, the last 10 to 15 years of research are revealing a clearer and more defined picture. Thus, the use of new experimental techniques and the results obtained provide new avenues for understanding the biology of the parasite, as well as opportunities for the development of much required new tools against malaria.

A workflow for annotating the knowledge gaps in metabolic reconstructions using known and hypothetical reactions

Advances in medicine and biotechnology rely on a deep understanding of biological processes. Despite the increasingly available types and amounts of omics data, significant knowledge gaps remain, with current approaches to identify and curate missing annotations being limited to a set of already known reactions. Here, we introduce Network Integrated Computational Explorer for Gap Annotation of Metabolism (NICEgame), a workflow to identify and curate nonannotated metabolic functions in genomes using the ATLAS of Biochemistry and genome-scale metabolic models (GEMs). To resolve gaps in GEMs, NICEgame provides alternative sets of known and hypothetical reactions, assesses their thermodynamic feasibility, and suggests candidate genes to catalyze these reactions. We identified metabolic gaps and applied NICEgame in the latest GEM of Escherichia coli, iML1515, and enhanced the E. coli genome annotation by resolving 47% of these gaps. NICEgame, applicable to any GEM and functioning from open-source software, should thus enhance all GEM-based predictions and subsequent biotechnological and biomedical applications.

Comparative analyses of parasites with a comprehensive database of genome-scale metabolic models

Protozoan parasites cause diverse diseases with large global impacts. Research on the pathogenesis and biology of these organisms is limited by economic and experimental constraints. Accordingly, studies of one parasite are frequently extrapolated to infer knowledge about another parasite, across and within genera. Model in vitro or in vivo systems are frequently used to enhance experimental manipulability, but these systems generally use species related to, yet distinct from, the clinically relevant causal pathogen. Characterization of functional differences among parasite species is confined to post hoc or single target studies, limiting the utility of this extrapolation approach. To address this challenge and to accelerate parasitology research broadly, we present a functional comparative analysis of 192 genomes, representing every high-quality, publicly-available protozoan parasite genome including Plasmodium, Toxoplasma, Cryptosporidium, Entamoeba, Trypanosoma, Leishmania, Giardia, and other species. We generated an automated metabolic network reconstruction pipeline optimized for eukaryotic organisms. These metabolic network reconstructions serve as biochemical knowledgebases for each parasite, enabling qualitative and quantitative comparisons of metabolic behavior across parasites. We identified putative differences in gene essentiality and pathway utilization to facilitate the comparison of experimental findings and discovered that phylogeny is not the sole predictor of metabolic similarity. This knowledgebase represents the largest collection of genome-scale metabolic models for both pathogens and eukaryotes; with this resource, we can predict species-specific functions, contextualize experimental results, and optimize selection of experimental systems for fastidious species.

Lee L, Alonso-Padilla J, Izquierdo L. Compounds targeting GPI biosynthesis or N-glycosylation are active against Plasmodium falciparum

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Compounds targeting key steps in GPI biosynthesis abrogate P. falciparum growth.

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N-glycosylation disruption halts parasite development and induces delayed death.

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Tunicamycin-induced delayed death is not linked with the synthesis of isoprenoids.

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In summary, two metabolic pathways are outlined for further drug target exploration.

The emergence of resistance to first-line antimalarials, including artemisinin, the last effective malaria therapy in some regions, stresses the urgent need to develop new effective treatments against this disease. The identification and validation of metabolic pathways that could be targeted for drug development may strongly contribute to accelerate this process. In this study, we use fully characterized specific inhibitors targeting glycan biosynthetic pathways as research tools to analyze their effects on the growth of the malaria parasite Plasmodium falciparum and to validate these metabolic routes as feasible chemotherapeutic targets. Through docking simulations using models predicted by AlphaFold, we also shed new light into the modes of action of some of these inhibitors. Molecules inhibiting N-acetylglucosaminyl-phosphatidylinositol de-N-acetylase (GlcNAc-PI de-N-acetylase, PIGL/GPI12) or the inositol acyltransferase (GWT1), central for glycosylphosphatidylinositol (GPI) biosynthesis, halt the growth of intraerythrocytic asexual parasites during the trophozoite stages of the intraerythrocytic developmental cycle (IDC). Remarkably, the nucleoside antibiotic tunicamycin, which targets UDP-N-acetylglucosamine:dolichyl-phosphate N-acetylglucosaminephosphotransferase (ALG7) and N-glycosylation in other organisms, induces a delayed-death effect and inhibits parasite growth during the second IDC after treatment. Our data indicate that tunicamycin induces a specific inhibitory effect, hinting to a more substantial role of the N-glycosylation pathway in P. falciparum intraerythrocytic asexual stages than previously thought. To sum up, our results place GPI biosynthesis and N-glycosylation pathways as metabolic routes with potential to yield much-needed therapeutic targets against the parasite.

Genome reconstructions of metabolism of Plasmodium RBC and liver stages

Genome scale metabolic models (GEMs) offer a powerful means of integrating genome and biochemical information on an organism to make testable predictions of metabolic functions at different conditions and to systematically predict essential genes that may be targeted by drugs. This review describes how Plasmodium GEMs have become increasingly more accurate through the integration of omics and experimental genetic data. We also discuss how GEMs contribute to our increasing understanding of how Plasmodium metabolism is reprogrammed between life cycle stages.

Reaction lumping in metabolic networks for application with thermodynamic metabolic flux analysis

Thermodynamic metabolic flux analysis (TMFA) can narrow down the space of steady-state flux distributions, but requires knowledge of the standard Gibbs free energy for the modelled reactions. The latter are often not available due to unknown Gibbs free energy change of formation [Formula: see text], of metabolites. To optimize the usage of data on thermodynamics in constraining a model, reaction lumping has been proposed to eliminate metabolites with unknown [Formula: see text]. However, the lumping procedure has not been formalized nor implemented for systematic identification of lumped reactions. Here, we propose, implement, and test a combined procedure for reaction lumping, applicable to genome-scale metabolic models. It is based on identification of groups of metabolites with unknown [Formula: see text] whose elimination can be conducted independently of the others via: (1) group implementation, aiming to eliminate an entire such group, and, if this is infeasible, (2) a sequential implementation to ensure that a maximal number of metabolites with unknown [Formula: see text] are eliminated. Our comparative analysis with genome-scale metabolic models of Escherichia coli, Bacillus subtilis, and Homo sapiens shows that the combined procedure provides an efficient means for systematic identification of lumped reactions. We also demonstrate that TMFA applied to models with reactions lumped according to the proposed procedure lead to more precise predictions in comparison to the original models. The provided implementation thus ensures the reproducibility of the findings and their application with standard TMFA.

Probabilistic thermodynamic analysis of metabolic networks

MOTIVATION

Random sampling of metabolic fluxes can provide a comprehensive description of the capabilities of a metabolic network. However, current sampling approaches do not model thermodynamics explicitly, leading to inaccurate predictions of an organism's potential or actual metabolic operations.

RESULTS

We present a probabilistic framework combining thermodynamic quantities with steady-state flux constraints to analyze the properties of a metabolic network. It includes methods for probabilistic metabolic optimization and for joint sampling of thermodynamic and flux spaces. Applied to a model of Escherichia coli, we use the methods to reveal known and novel mechanisms of substrate channeling, and to accurately predict reaction directions and metabolite concentrations. Interestingly, predicted flux distributions are multimodal, leading to discrete hypotheses on E.coli's metabolic capabilities.

AVAILABILITY AND IMPLEMENTATION

Python and MATLAB packages available at https://gitlab.com/csb.ethz/pta.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

PhenoMapping: a protocol to map cellular phenotypes to metabolic bottlenecks, identify conditional essentiality, and curate metabolic models

Targeted identification of cellular processes responsible for a phenotype is of major importance in guiding efforts in bioengineering and medicine. Genome-scale metabolic models (GEMs) are widely used to integrate various types of omics data and study the cellular physiology under different conditions. Here, we present PhenoMapping, a protocol that uses GEMs, omics, and phenotypic data to map cellular processes and observed phenotypes. PhenoMapping also classifies genes as conditionally and unconditionally essential and guides a comprehensive curation of GEMs. For complete details on the use and execution of this protocol, please refer to Stanway et al. (2019) and Krishnan et al. (2020).

Amino Acid Metabolism in Apicomplexan Parasites

Obligate intracellular pathogens have coevolved with their host, leading to clever strategies to access nutrients, to combat the host's immune response, and to establish a safe niche for intracellular replication. The host, on the other hand, has also developed ways to restrict the replication of invaders by limiting access to nutrients required for pathogen survival. In this review, we describe the recent advancements in both computational methods and high-throughput -omics techniques that have been used to study and interrogate metabolic functions in the context of intracellular parasitism. Specifically, we cover the current knowledge on the presence of amino acid biosynthesis and uptake within the Apicomplexa phylum, focusing on human-infecting pathogens: Toxoplasma gondii and Plasmodium falciparum. Given the complex multi-host lifecycle of these pathogens, we hypothesize that amino acids are made, rather than acquired, depending on the host niche. We summarize the stage specificities of enzymes revealed through transcriptomics data, the relevance of amino acids for parasite pathogenesis in vivo, and the role of their transporters. Targeting one or more of these pathways may lead to a deeper understanding of the specific contributions of biosynthesis versus acquisition of amino acids and to design better intervention strategies against the apicomplexan parasites.

Plasmodium falciparum Apicomplexan-Specific Glucosamine-6-Phosphate N-Acetyltransferase Is Key for Amino Sugar Metabolism and Asexual Blood Stage Development

Apicomplexan parasites cause a major burden on global health and economy. The absence of treatments, the emergence of resistances against available therapies, and the parasite’s ability to manipulate host cells and evade immune systems highlight the urgent need to characterize new drug targets to treat infections caused by these parasites. We demonstrate that glucosamine-6-phosphate N-acetyltransferase (GNA1), required for the biosynthesis of UDP-N-acetylglucosamine (UDP-GlcNAc), is essential for P. falciparum asexual blood stage development and that the disruption of the gene encoding this enzyme quickly causes the death of the parasite within a life cycle. The high-resolution crystal structure of the GNA1 ortholog from the apicomplexan parasite C. parvum, used here as a surrogate, highlights significant differences from human GNA1. These divergences can be exploited for the design of specific inhibitors against the malaria parasite.

UDP-N-acetylglucosamine (UDP-GlcNAc), the main product of the hexosamine biosynthetic pathway, is an important metabolite in protozoan parasites since its sugar moiety is incorporated into glycosylphosphatidylinositol (GPI) glycolipids and N- and O-linked glycans. Apicomplexan parasites have a hexosamine pathway comparable to other eukaryotic organisms, with the exception of the glucosamine-phosphate N-acetyltransferase (GNA1) enzymatic step that has an independent evolutionary origin and significant differences from nonapicomplexan GNA1s. By using conditional genetic engineering, we demonstrate the requirement of GNA1 for the generation of a pool of UDP-GlcNAc and for the development of intraerythrocytic asexual Plasmodium falciparum parasites. Furthermore, we present the 1.95 Å resolution structure of the GNA1 ortholog from Cryptosporidium parvum, an apicomplexan parasite which is a leading cause of diarrhea in developing countries, as a surrogate for P. falciparum GNA1. The in-depth analysis of the crystal shows the presence of specific residues relevant for GNA1 enzymatic activity that are further investigated by the creation of site-specific mutants. The experiments reveal distinct features in apicomplexan GNA1 enzymes that could be exploitable for the generation of selective inhibitors against these parasites, by targeting the hexosamine pathway. This work underscores the potential of apicomplexan GNA1 as a drug target against malaria.

Modeling the metabolic interplay between a parasitic worm and its bacterial endosymbiont allows the identification of novel drug targets

The filarial nematode Brugia malayi represents a leading cause of disability in the developing world, causing lymphatic filariasis in nearly 40 million people. Currently available drugs are not well-suited to mass drug administration efforts, so new treatments are urgently required. One potential vulnerability is the endosymbiotic bacteria Wolbachia-present in many filariae-which is vital to the worm. Genome scale metabolic networks have been used to study prokaryotes and protists and have proven valuable in identifying therapeutic targets, but have only been applied to multicellular eukaryotic organisms more recently. Here, we present iDC625, the first compartmentalized metabolic model of a parasitic worm. We used this model to show how metabolic pathway usage allows the worm to adapt to different environments, and predict a set of 102 reactions essential to the survival of B. malayi. We validated three of those reactions with drug tests and demonstrated novel antifilarial properties for all three compounds.

Analysis of human metabolism by reducing the complexity of the genome-scale models using redHUMAN

Altered metabolism is associated with many human diseases. Human genome-scale metabolic models (GEMs) were reconstructed within systems biology to study the biochemistry occurring in human cells. However, the complexity of these networks hinders a consistent and concise physiological representation. We present here redHUMAN, a workflow for reconstructing reduced models that focus on parts of the metabolism relevant to a specific physiology using the recently established methods redGEM and lumpGEM. The reductions include the thermodynamic properties of compounds and reactions guaranteeing the consistency of predictions with the bioenergetics of the cell. We introduce a method (redGEMX) to incorporate the pathways used by cells to adapt to the medium. We provide the thermodynamic curation of the human GEMs Recon2 and Recon3D and we apply the redHUMAN workflow to derive leukemia-specific reduced models. The reduced models are powerful platforms for studying metabolic differences between phenotypes, such as diseased and healthy cells.

Genome-Scale Identification of Essential Metabolic Processes for Targeting the Plasmodium Liver Stage

Plasmodium gene functions in mosquito and liver stages remain poorly characterized due to limitations in the throughput of phenotyping at these stages. To fill this gap, we followed more than 1,300 barcoded P. berghei mutants through the life cycle. We discover 461 genes required for efficient parasite transmission to mosquitoes through the liver stage and back into the bloodstream of mice. We analyze the screen in the context of genomic, transcriptomic, and metabolomic data by building a thermodynamic model of P. berghei liver-stage metabolism, which shows a major reprogramming of parasite metabolism to achieve rapid growth in the liver. We identify seven metabolic subsystems that become essential at the liver stages compared with asexual blood stages: type II fatty acid synthesis and elongation (FAE), tricarboxylic acid, amino sugar, heme, lipoate, and shikimate metabolism. Selected predictions from the model are individually validated in single mutants to provide future targets for drug development.

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1,342 barcoded P. berghei knockout (KO) mutants analyzed for stage-specific phenotypes

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Life-stage-specific metabolic models reveal reprogramming of cellular function

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High agreement between blood/liver stage metabolic models and genetic screening data

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Essential metabolic pathways for parasite development and mechanistic origin revealed

An Omnivore's Dilemma: Toxoplasma gondii's Flexible Metabolic Networks

Host cells provide protection from the environment for intracellular pathogens, but acquisition of nutrients and sensing of changes in the host cell bring new challenges. Krishnan et al. have recently developed a comprehensive genome-scale metabolic model (GEM) of the Toxoplasma gondii metabolic network that incorporates genetic, transcriptomic, and metabolomic data.

Large-scale kinetic metabolic models of Pseudomonas putida KT2440 for consistent design of metabolic engineering strategies

BACKGROUND

Pseudomonas putida is a promising candidate for the industrial production of biofuels and biochemicals because of its high tolerance to toxic compounds and its ability to grow on a wide variety of substrates. Engineering this organism for improved performances and predicting metabolic responses upon genetic perturbations requires reliable descriptions of its metabolism in the form of stoichiometric and kinetic models.

RESULTS

In this work, we developed kinetic models of P. putida to predict the metabolic phenotypes and design metabolic engineering interventions for the production of biochemicals. The developed kinetic models contain 775 reactions and 245 metabolites. Furthermore, we introduce here a novel set of constraints within thermodynamics-based flux analysis that allow for considering concentrations of metabolites that exist in several compartments as separate entities. We started by a gap-filling and thermodynamic curation of iJN1411, the genome-scale model of P. putida KT2440. We then systematically reduced the curated iJN1411 model, and we created three core stoichiometric models of different complexity that describe the central carbon metabolism of P. putida. Using the medium complexity core model as a scaffold, we generated populations of large-scale kinetic models for two studies. In the first study, the developed kinetic models successfully captured the experimentally observed metabolic responses to several single-gene knockouts of a wild-type strain of P. putida KT2440 growing on glucose. In the second study, we used the developed models to propose metabolic engineering interventions for improved robustness of this organism to the stress condition of increased ATP demand.

CONCLUSIONS

The study demonstrates the potential and predictive capabilities of the kinetic models that allow for rational design and optimization of recombinant P. putida strains for improved production of biofuels and biochemicals. The curated genome-scale model of P. putida together with the developed large-scale stoichiometric and kinetic models represents a significant resource for researchers in industry and academia.

Mining the Human Host Metabolome Toward an Improved Understanding of Malaria Transmission

The big data movement has led to major advances in our ability to assess vast and complex datasets related to the host and parasite during malaria infection. While host and parasite genomics and transcriptomics are often the focus of many computational efforts in malaria research, metabolomics represents another big data type that has great promise for aiding our understanding of complex host-parasite interactions that lead to the transmission of malaria. Recent analyses of the complement of metabolites present in human blood, skin and breath suggest that host metabolites play a critical role in the transmission cycle of malaria. Volatile compounds released through breath and skin serve as attractants to mosquitoes, with malaria-infected hosts appearing to have unique profiles that further increase host attractiveness. Inside the host, fluctuations in the levels of certain metabolites in blood may trigger increased production of transmission-competent sexual stages (gametocytes), setting the stage for enhanced transmission of malaria from human to mosquito. Together, these recent discoveries suggest that metabolites of human blood, skin and breath play critical roles in malaria transmission. This review discusses recent advances in this area, with a focus on metabolites that have been identified to play a role in malaria transmission and methods that may lead to an improved understanding of malaria transmission.

Functional and Computational Genomics Reveal Unprecedented Flexibility in Stage-Specific Toxoplasma Metabolism

To survive and proliferate in diverse host environments with varying nutrient availability, the obligate intracellular parasite Toxoplasma gondii reprograms its metabolism. We have generated and curated a genome-scale metabolic model (iTgo) for the fast-replicating tachyzoite stage, harmonized with experimentally observed phenotypes. To validate the importance of four metabolic pathways predicted by the model, we have performed in-depth in vitro and in vivo phenotyping of mutant parasites including targeted metabolomics and CRISPR-Cas9 fitness screening of all known metabolic genes. This led to unexpected insights into the remarkable flexibility of the parasite, addressing the dependency on biosynthesis or salvage of fatty acids (FAs), purine nucleotides (AMP and GMP), a vitamin (pyridoxal-5P), and a cofactor (heme) in both the acute and latent stages of infection. Taken together, our experimentally validated metabolic network leads to a deeper understanding of the parasite's biology, opening avenues for the development of therapeutic intervention against apicomplexans.

Vitamin and cofactor acquisition in apicomplexans: Synthesis versus salvage

The Apicomplexa phylum comprises diverse parasitic organisms that have evolved from a free-living ancestor. These obligate intracellular parasites exhibit versatile metabolic capabilities reflecting their capacity to survive and grow in different hosts and varying niches. Determined by nutrient availability, they either use their biosynthesis machineries or largely depend on their host for metabolite acquisition. Because vitamins cannot be synthesized by the mammalian host, the enzymes required for their synthesis in apicomplexan parasites represent a large repertoire of potential therapeutic targets. Here, we review recent advances in metabolic reconstruction and functional studies coupled to metabolomics that unravel the interplay between biosynthesis and salvage of vitamins and cofactors in apicomplexans. A particular emphasis is placed on Toxoplasma gondii, during both its acute and latent stages of infection.

Mechanistic insights into bacterial metabolic reprogramming from omics-integrated genome-scale models

Understanding the adaptive responses of individual bacterial strains is crucial for microbiome engineering approaches that introduce new functionalities into complex microbiomes, such as xenobiotic compound metabolism for soil bioremediation. Adaptation requires metabolic reprogramming of the cell, which can be captured by multi-omics, but this data remains formidably challenging to interpret and predict. Here we present a new approach that combines genome-scale metabolic modeling with transcriptomics and exometabolomics, both of which are common tools for studying dynamic population behavior. As a realistic demonstration, we developed a genome-scale model of Pseudomonas veronii 1YdBTEX2, a candidate bioaugmentation agent for accelerated metabolism of mono-aromatic compounds in soil microbiomes, while simultaneously collecting experimental data of P. veronii metabolism during growth phase transitions. Predictions of the P. veronii growth rates and specific metabolic processes from the integrated model closely matched experimental observations. We conclude that integrative and network-based analysis can help build predictive models that accurately capture bacterial adaptation responses. Further development and testing of such models may considerably improve the successful establishment of bacterial inoculants in more complex systems.

pyTFA and matTFA: a Python package and a Matlab toolbox for Thermodynamics-based Flux Analysis

Summary

pyTFA and matTFA are the first published implementations of the original TFA paper. Specifically, they include explicit formulation of Gibbs energies and metabolite concentrations, which enables straightforward integration of metabolite concentration measurements.

Motivation

High-throughput analytic technologies provide a wealth of omics data that can be used to perform thorough analyses for a multitude of studies in the areas of Systems Biology and Biotechnology. Nevertheless, most studies are still limited to constraint-based Flux Balance Analyses (FBA), neglecting an important physicochemical constraint: thermodynamics. Thermodynamics-based Flux Analysis (TFA) in metabolic models enables the integration of quantitative metabolomics data to study their effects on the net-flux directionality of reactions in the network. In addition, it allows us to estimate how far each reaction operates from thermodynamic equilibrium, which provides critical information for guiding metabolic engineering decisions.

Results

We present a Python package (pyTFA) and a Matlab toolbox (matTFA) that implement TFA. We show an example of application on both a reduced and a genome-scale model of E. coli., and demonstrate TFA and data integration through TFA reduce the feasible flux space with respect to FBA.

Availability and implementation

Documented implementation of TFA framework both in Python (pyTFA) and Matlab (matTFA) are available on www.github.com/EPFL-LCSB/.

Supplementary information

Supplementary data are available at Bioinformatics online.

Computational Identification of Metabolic Pathways of Plasmodium falciparum using the k-Shortest Path Algorithm

Plasmodium falciparum, a malaria pathogen, has shown substantial resistance to treatment coupled with poor response to some vaccines thereby requiring urgent, holistic, and broad approach to prevent this endemic disease. Understanding the biology of the malaria parasite has been identified as a vital approach to overcome the threat of malaria. This study is aimed at identifying essential proteins unique to malaria parasites using a reconstructed iPfa genome-scale metabolic model (GEM) of the 3D7 strain of Plasmodium falciparum by filling gaps in the model with nineteen (19) metabolites and twenty-three (23) reactions obtained from the MetaCyc database. Twenty (20) currency metabolites were removed from the network because they have been identified to produce shortcuts that are biologically infeasible. The resulting modified iPfa GEM was a model using the k-shortest path algorithm to identify possible alternative metabolic pathways in glycolysis and pentose phosphate pathways of Plasmodium falciparum. Heuristic function was introduced for the optimal performance of the algorithm. To validate the prediction, the essentiality of the reactions in the reconstructed network was evaluated using betweenness centrality measure, which was applied to every reaction within the pathways considered in this study. Thirty-two (32) essential reactions were predicted among which our method validated fourteen (14) enzymes already predicted in the literature. The enzymatic proteins that catalyze these essential reactions were checked for homology with the host genome, and two (2) showed insignificant similarity, making them possible drug targets. In conclusion, the application of the intelligent search technique to the metabolic network of P. falciparum predicts potential biologically relevant alternative pathways using graph theory-based approach.

Adrenal hormones mediate disease tolerance in malaria

Malaria reduces host fitness and survival by pathogen-mediated damage and inflammation. Disease tolerance mechanisms counter these negative effects without decreasing pathogen load. Here, we demonstrate that in four different mouse models of malaria, adrenal hormones confer disease tolerance and protect against early death, independently of parasitemia. Surprisingly, adrenalectomy differentially affects malaria-induced inflammation by increasing circulating cytokines and inflammation in the brain but not in the liver or lung. Furthermore, without affecting the transcription of hepatic gluconeogenic enzymes, adrenalectomy causes exhaustion of hepatic glycogen and insulin-independent lethal hypoglycemia upon infection. This hypoglycemia is not prevented by glucose administration or TNF-α neutralization. In contrast, treatment with a synthetic glucocorticoid (dexamethasone) prevents the hypoglycemia, lowers cerebral cytokine expression and increases survival rates. Overall, we conclude that in malaria, adrenal hormones do not protect against lung and liver inflammation. Instead, they prevent excessive systemic and brain inflammation and severe hypoglycemia, thereby contributing to tolerance.

Disease tolerance mechanisms counter the negative effects of infection without decreasing the pathogen load. Here, the authors show that in mouse models of malaria, such disease tolerance can be conferred by adrenal hormones, by preventing excessive inflammation and hypoglycemia.

Effects of microcompartmentation on flux distribution and metabolic pools in Chlamydomonas reinhardtii chloroplasts

Cells and organelles are not homogeneous but include microcompartments that alter the spatiotemporal characteristics of cellular processes. The effects of microcompartmentation on metabolic pathways are however difficult to study experimentally. The pyrenoid is a microcompartment that is essential for a carbon concentrating mechanism (CCM) that improves the photosynthetic performance of eukaryotic algae. Using Chlamydomonas reinhardtii, we obtained experimental data on photosynthesis, metabolites, and proteins in CCM-induced and CCM-suppressed cells. We then employed a computational strategy to estimate how fluxes through the Calvin-Benson cycle are compartmented between the pyrenoid and the stroma. Our model predicts that ribulose-1,5-bisphosphate (RuBP), the substrate of Rubisco, and 3-phosphoglycerate (3PGA), its product, diffuse in and out of the pyrenoid, respectively, with higher fluxes in CCM-induced cells. It also indicates that there is no major diffusional barrier to metabolic flux between the pyrenoid and stroma. Our computational approach represents a stepping stone to understanding microcompartmentalized CCM in other organisms.

Rodent malaria models: insights into human disease and parasite biology

The use of rodents as model organisms to study human disease is based on the genetic and physiological similarities between the species. Successful molecular methods to generate transgenic reporter or humanized rodents has rendered rodents as powerful tools for understanding biological processes and host-pathogen interactions relevant to humans. In malaria research, rodent models have been pivotal for the study of liver stages, syndromes arising from blood stages of infection, and malaria transmission to and from the mammalian host. Importantly, many in vivo findings are comparable to pathology observed in humans only when adequate combinations of rodent strains and Plasmodium parasites are used.

RAVEN 2.0: A versatile toolbox for metabolic network reconstruction and a case study on Streptomyces coelicolor

RAVEN is a commonly used MATLAB toolbox for genome-scale metabolic model (GEM) reconstruction, curation and constraint-based modelling and simulation. Here we present RAVEN Toolbox 2.0 with major enhancements, including: (i) de novo reconstruction of GEMs based on the MetaCyc pathway database; (ii) a redesigned KEGG-based reconstruction pipeline; (iii) convergence of reconstructions from various sources; (iv) improved performance, usability, and compatibility with the COBRA Toolbox. Capabilities of RAVEN 2.0 are here illustrated through de novo reconstruction of GEMs for the antibiotic-producing bacterium Streptomyces coelicolor. Comparison of the automated de novo reconstructions with the iMK1208 model, a previously published high-quality S. coelicolor GEM, exemplifies that RAVEN 2.0 can capture most of the manually curated model. The generated de novo reconstruction is subsequently used to curate iMK1208 resulting in Sco4, the most comprehensive GEM of S. coelicolor, with increased coverage of both primary and secondary metabolism. This increased coverage allows the use of Sco4 to predict novel genome editing targets for optimized secondary metabolites production. As such, we demonstrate that RAVEN 2.0 can be used not only for de novo GEM reconstruction, but also for curating existing models based on up-to-date databases. Both RAVEN 2.0 and Sco4 are distributed through GitHub to facilitate usage and further development by the community (https://github.com/SysBioChalmers/RAVEN and https://github.com/SysBioChalmers/Streptomyces_coelicolor-GEM).

In Silico Knockout Screening of Plasmodium falciparum Reactions and Prediction of Novel Essential Reactions by Analysing the Metabolic Network

Malaria is an infectious disease that affects close to half a million individuals every year and Plasmodium falciparum is a major cause of malaria. The treatment of this disease could be done effectively if the essential enzymes of this parasite are specifically targeted. Nevertheless, the development of the parasite in resisting existing drugs now makes discovering new drugs a core responsibility. In this study, a novel computational model that makes the prediction of new and validated antimalarial drug target cheaper, easier, and faster has been developed. We have identified new essential reactions as potential targets for drugs in the metabolic network of the parasite. Among the top seven (7) predicted essential reactions, four (4) have been previously identified in earlier studies with biological evidence and one (1) has been with computational evidence. The results from our study were compared with an extensive list of seventy-seven (77) essential reactions with biological evidence from a previous study. We present a list of thirty-one (31) potential candidates for drug targets in Plasmodium falciparum which includes twenty-four (24) new potential candidates for drug targets.

The Apicomplexa-specific glucosamine-6-phosphate N-acetyltransferase gene family encodes a key enzyme for glycoconjugate synthesis with potential as therapeutic target

Apicomplexa form a phylum of obligate parasitic protozoa of great clinical and veterinary importance. These parasites synthesize glycoconjugates for their survival and infectivity, but the enzymatic steps required to generate the glycosylation precursors are not completely characterized. In particular, glucosamine-phosphate N-acetyltransferase (GNA1) activity, needed to produce the essential UDP-N-acetylglucosamine (UDP-GlcNAc) donor, has not been identified in any Apicomplexa. We scanned the genomes of Plasmodium falciparum and representatives from six additional main lineages of the phylum for proteins containing the Gcn5-related N-acetyltransferase (GNAT) domain. One family of GNAT-domain containing proteins, composed by a P. falciparum sequence and its six apicomplexan orthologs, rescued the growth of a yeast temperature-sensitive GNA1 mutant. Heterologous expression and in vitro assays confirmed the GNA1 enzymatic activity in all lineages. Sequence, phylogenetic and synteny analyses suggest an independent origin of the Apicomplexa-specific GNA1 family, parallel to the evolution of a different GNA1 family in other eukaryotes. The inability to disrupt an otherwise modifiable gene target suggests that the enzyme is essential for P. falciparum growth. The relevance of UDP-GlcNAc for parasite viability, together with the independent evolution and unique sequence features of Apicomplexa GNA1, highlights the potential of this enzyme as a selective therapeutic target against apicomplexans.

Functional interrogation of Plasmodium genus metabolism identifies species- and stage-specific differences in nutrient essentiality and drug targeting

Several antimalarial drugs exist, but differences between life cycle stages among malaria species pose challenges for developing more effective therapies. To understand the diversity among stages and species, we reconstructed genome-scale metabolic models (GeMMs) of metabolism for five life cycle stages and five species of Plasmodium spanning the blood, transmission, and mosquito stages. The stage-specific models of Plasmodium falciparum uncovered stage-dependent changes in central carbon metabolism and predicted potential targets that could affect several life cycle stages. The species-specific models further highlight differences between experimental animal models and the human-infecting species. Comparisons between human- and rodent-infecting species revealed differences in thiamine (vitamin B1), choline, and pantothenate (vitamin B5) metabolism. Thus, we show that genome-scale analysis of multiple stages and species of Plasmodium can prioritize potential drug targets that could be both anti-malarials and transmission blocking agents, in addition to guiding translation from non-human experimental disease models.

Malaria kills nearly one-half million people a year and over 1 billion people are at risk of becoming infected by the parasite. Plasmodial infections are difficult to treat for a myriad of reasons, but the ability of the organism to remain latent in hosts and the complex life cycles greatly contributed to the difficulty in treat malaria. Genome-scale metabolic models (GeMMs) enable hierarchical integration of disparate data types into a framework amenable to computational simulations enabling deeper mechanistic insights from high-throughput data measurements. In this study, GeMMs of multiple Plasmodium species are used to study metabolic similarities and differences across the Plasmodium genus. In silico gene-knock out simulations across species and stages uncovered functional metabolic differences between human- and rodent-infecting species as well as across the parasite’s life-cycle stages. These findings may help identify drug regimens that are more effective in targeting human-infecting species across multiple stages of the organism.

Novel Plasmodium falciparum metabolic network reconstruction identifies shifts associated with clinical antimalarial resistance

Background

Malaria remains a major public health burden and resistance has emerged to every antimalarial on the market, including the frontline drug, artemisinin. Our limited understanding of Plasmodium biology hinders the elucidation of resistance mechanisms. In this regard, systems biology approaches can facilitate the integration of existing experimental knowledge and further understanding of these mechanisms.

Results

Here, we developed a novel genome-scale metabolic network reconstruction, iPfal17, of the asexual blood-stage P. falciparum parasite to expand our understanding of metabolic changes that support resistance. We identified 11 metabolic tasks to evaluate iPfal17 performance. Flux balance analysis and simulation of gene knockouts and enzyme inhibition predict candidate drug targets unique to resistant parasites. Moreover, integration of clinical parasite transcriptomes into the iPfal17 reconstruction reveals patterns associated with antimalarial resistance. These results predict that artemisinin sensitive and resistant parasites differentially utilize scavenging and biosynthetic pathways for multiple essential metabolites, including folate and polyamines. Our findings are consistent with experimental literature, while generating novel hypotheses about artemisinin resistance and parasite biology. We detect evidence that resistant parasites maintain greater metabolic flexibility, perhaps representing an incomplete transition to the metabolic state most appropriate for nutrient-rich blood.

Conclusion

Using this systems biology approach, we identify metabolic shifts that arise with or in support of the resistant phenotype. This perspective allows us to more productively analyze and interpret clinical expression data for the identification of candidate drug targets for the treatment of resistant parasites.

Electronic supplementary material

The online version of this article (doi:10.1186/s12864-017-3905-1) contains supplementary material, which is available to authorized users.