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

Novel antimalarial therapies are urgently needed for the fight against drug-resistant para- sites. The metabolism of malaria parasites in infected cells is an attractive source of drug targets but is rather complex. Computational methods can handle this complexity and allow integrative analyses of cell metabolism. In this study, we present a genome-scale metabolic model (iPfa) of the deadliest malaria parasite, Plasmodium falciparum, and its thermody- namics-based flux analysis (TFA). Using previous absolute concentration data of the intraer- ythrocytic parasite, we applied TFA to iPfa and predicted up to 63 essential genes and 26 essential pairs of genes. Of the 63 genes, 35 have been experimentally validated and reported in the literature, and 28 have not been experimentally tested and include previously hypothesized or novel predictions of essential metabolic capabilities. Without metabolomics data, four of the genes would have been incorrectly predicted to be non-essential. TFA also indicated that substrate channeling should exist in two metabolic pathways to ensure the thermodynamic feasibility of the flux. Finally, analysis of the metabolic capabilities of P. fal- ciparum led to the identification of both the minimal nutritional requirements and the genes that can become indispensable upon substrate inaccessibility. This model provides novel insight into the metabolic needs and capabilities of the malaria parasite and highlights metabolites and pathways that should be measured and characterized to identify potential thermodynamic bottlenecks and substrate channeling. The hypotheses presented seek to guide experimental studies to facilitate a better understanding of the parasite metabolism and the identification of targets for more efficient intervention.

Analysis of cellular and drug action mechanisms in the malaria parasites using systems biology approaches

Anush Chiappino-Pepe

Malaria puts at risk of infection half of the world population and still kills around half a million people every year. The rapid and worrisome rise of drug-resistant malaria parasites hinders the treatment of malaria and calls for a global collaboration to eradicate this disease. The technological advances have allowed sequencing the genome and measuring metabolites, proteins, and mRNAs of the parasites at a high-throughput, which has provided insights into the biological processes of these organisms. However, many aspects of the biology of the malaria parasites remain hidden and hinder the development of effective and selective antimalarial therapies. The understanding of the cell function as a whole rather than the study of its components in isolation will enable the development of strategies to engineer and target the cell function. Only computational tools can provide a comprehensive understanding of the cellular function in different contexts and upon perturbation. Moreover, computational methods provide testable hypotheses that can guide and accelerate experimental discoveries. Metabolism is to date the most characterized biological process in the cell and is an attractive source of drug targets. Metabolic networks encompass all available information on the biochemistry of an organism and represent an attractive scaffold to integrate omics data and analyze complex metabolic processes. In this thesis, we present the metabolic models of two malaria parasites, i.e., Plasmodium falciparum and Plasmodium berghei, and a set of mathematical and computational methods for the comprehensive and unbiased analysis of metabolic networks. We focus on the bioenergetics-based and metabolic control analysis of the malaria parasitesâ metabolism to find drug targets and investigate drug action mechanisms. Firstly, we develop computational methods that follow principles from network thermodynamics to identify essential metabolic functions, thermodynamic bottlenecks, and nutritional requirements of P. falciparum. Secondly, we define a mathematical framework for Phenotype Mapping (PhenoMapping), which associates a cellular phenotype with the underlying cellular processes and conditions. We applied PhenoMapping to investigate the PlasmoGEM phenotypes obtained for P. berghei in the blood stages of its life cycle. This analysis enables the identification of non-conditionally and conditionally essential metabolic functions and the development of context-specific metabolic models. Thirdly, we present a Mixed Integer Synthetic Lethal Enumeration (MISyLE) method for the optimal identification of essential and all redundant (synthetic lethal) sets of reactions and genes for a cellular phenotype. Fourthly, we investigate the effect of antimalarial-based therapies with one or multiple antifolates in the metabolism of P. falciparum in the blood stages, and we examine new drug targeting strategies that might maximize the impact on the intraerythrocytic development of the parasites. Fifthly, we discuss a systems biology approach to accelerate our understanding of biological systems based on the disagreements between in silico and in vivo observations. Finally, we suggest strategies to use the models and computational frameworks developed in this thesis to further investigate host-pathogen interactions, drug action mechanisms, and dormancy in the malaria parasites and other organisms.

EPFL2018

Optimization methods and objective functions for analysis of metabolic network models

Zhaleh Hosseini

Computational studies of metabolism aim to systematically analyze the metabolic behaviour of biological systems in different conditions. Genome-scale metabolic network models (GEMs) capture the connection between elements of the network by applying stoichiometric balances while taking into account gene-protein-reaction associations. Several methods have already been developed for the analysis of these models. These methods are mainly used to optimize a single or combination of objective functions subject to different constraints. In this thesis, we have summarized the optimization methods and objective functions by classifying them based on biological and mathematical features. Particularly, we suggest reformulations to convert some of the complex optimization classes to simpler ones. One of the reformulations is the conversion of mixed-integer linear fractional programming (MILFP) to mixed-integer linear programming (MILP). We show that this conversion is useful in studying coupling relationships in thermodynamically constrained metabolic network models. Coupling determines how different components of the network such as metabolites or reactions are interrelated. Particularly, flux Coupling Analysis (FCA) is a method for evaluating the dependencies between metabolic reactions. In FCA, two reactions are considered as coupled if the activity of one, constrains the activity of the other. So far, FCA has been used for analyzing metabolic reactions in flux-balanced models. In this work, we developed a new formulation, Thermodynamic Flux Coupling Analysis (TFCA), which calculates flux couplings of metabolic models that are subjected to thermodynamic constraints. With TFCA, we show that adding thermodynamic constraints can significantly change the coupling relationship of reactions of the network. Moreover, we show that calculating coupling relations helps in reducing the number of combinations of bidirectional reactions (BDRs), which in turn will facilitate the analysis of the metabolic network. In addition to proposing several mathematical reformulations to gain global optimality, we also addressed the issue of finding the proper cellular objective function in different conditions of cellular metabolism. This is not always straight-forward, since the metabolic activities of some organisms are not well-characterized, e.g., metabolism of dormancy phase in some bacteria and parasites. In this thesis, we studied the metabolic behaviour of dormant malaria parasite using genome-scale model of Plasmodium falciparum. We examined known and novel objective functions and scored them based on the modelâs consistency with experimental gene expression data. Our results suggested that minimizing energy dissipation can best describe the metabolic activities of the malaria parasites in the dormancy phase. In the last chapter, we focus on studying another poorly characterized metabolic system that is the process of iron reduction in Clostridium acetobutylicum. Research has shown that this organism can reduce Fe(III), but the mechanism behind this reduction is yet to be identified. In this thesis, we analyzed the metabolism of C. acetobutylicum using its reconstructed genome-scale metabolic network model and experimental transcriptomics data in the presence or absence of Fe(III). By performing several computational studies, we suggested that NAD(P) is involved in the reduction of iron and is the potential physiological electron donor to Fe(III).

EPFL2020

Metabolic modeling and experimental studies on carbon and energy metabolism of Mycobacterium tuberculosis

Muhittin Emre Özdemir

Tuberculosis is an ancient human disease affecting millions of people every year. The common causative agent, Mycobacterium tuberculosis, is a peculiar pathogen that can persist in the host tissues for decades. Many of the key attributes of M. tuberculosis can be traced to its metabolism. Therefore, novel insights into the metabolism of this bacterium could lead to the identification of new drug targets as well as better methods to understand the mechanism of action of anti-tuberculosis drugs. Analysis of the metabolic network of this bacterium using a genome-scale model offers an unconventional approach to understanding utilization of metabolic pathways of this pathogen during growth and non-replicating persistence. Metabolic adaptation of a pathogen is central to its life style in vivo. To understand the capacity and limitations of M. tuberculosis, we developed an updated and thermodynamically curated genome scale model – iOS783. This model has been verified with respect to the observed phenotypes for targeted gene deletion studies reported previously. Evaluation of iOS783 with thermodynamics based flux balance analysis (TFBA) improved overall reliability of the model results and led to identification of possible bottleneck reactions. In addition, several new hypotheses were generated with iOS783, involving the role of isocitrate lyase enzymes, propionate toxicity, and energy metabolism of this pathogen. Analysis of iOS783 for conditional gene essentiality emphasizes the importance of the carbon source available to the pathogen. Several genes show a conditional impact on biomass production rate of the model, depending on the availability of carbohydrates or fatty acids as the carbon substrate. Conditional essentiality is even more pronounced when ATP replenishment capacity is used as a measure of a pathogen’s ability to survive under stress in a non-growing state. As expected, ATP synthase plays a major role in estimations of ATP replenishment capacity based on iOS783. Curiously, computational analysis of the metabolic network with ATP synthase activity blocked indicates that the damaged network can use substrate-level phosphorylation reactions to sustain ATP production to fulfill non-growth-associated ATP requirements when glucose is available. In order to establish the dependence of M. tuberculosis on ATP synthase, we attempted to construct a deletion of the chromosomal atp operon. Our results show that the native copy of these genes can be deleted only when a second set of genes are introduced elsewhere in the genome, indicating essentiality of the ATP synthase for growth. Although our results indicate that ATP synthase is essential for growth, we observed carbon source specific efficacy of an ATP synthase inhibitor, bedaquiline, on M. tuberculosis. These experimental results are consistent with ATP turnover estimations based on modeling results. Furthermore, analysis of iOS783 suggests a novel role in energy metabolism for recently described glycine dehydrogenase (GDH) on energy metabolism. Deletion of ald gene responsible for GDH activity reduces the ATP replenishment capacity of iOS783, especially during metabolism of fatty acid derivative carbon sources. Consistent with these modeling results, wetlab experiments designed to test this hypothesis show that bedaquiline has a stronger bactericidal effect on an M. tuberculosis ∆ald mutant, compared to the wild-type parental strain, when cells are cultured in medium containing acetate as the carbon source. Metabolic modelling is already well established as an invaluable tool for engineering of strains with desired characteristics. For pathogenic species, however, the application of this approach has so far been quite limited. The work presented in this thesis exemplifies an interdisciplinary approach combining computational modeling and experimental studies of M. tuberculosis metabolism. First, iOS783 was utilized to interpret existing experimental results and to generate novel hypotheses, thereby providing the impetus for new wetlab experiments. Conversely, results from iOS783-inspired wetlab experiments were used to further refine and expand the iOS783 model. This cyclic exchange between computational modeling and wetlab experimentation illustrates how a close interdisciplinary partnership can create a positive feedback loop that enriches both disciplines.

EPFL2013