Reconstruction and systems analysis of metabolism in apicomplexan parasites Toxoplasma gondii and Plasmodium falciparum

Understanding of metabolism in disease-causing microorganisms promotes drug design through the identification of the enzymes whose activity is indispensable for important cellular functions of the pathogens. Nowadays such understanding arises from experimental as well as computational studies. These two approaches, long considered as rather orthogonal, in recent years began to converge and form a new field, where they are utilized as complementary. In this thesis I present my endeavors in bringing closer the fields of infection and systems biology with a particular focus on large-scale metabolic models and their analysis. Integrative, interdisciplinary nature of my project also included multiple experimental inputs as well as original experimental efforts on investigating model-derived hypotheses. In the scope of this thesis I explored metabolism of two of the most experimentally amenable apicomplexan species â human parasites Plasmodium falciparum and Toxoplasma gondii. As a foundation for the studies included in this thesis I used standard as well as recently developed computational algorithms, existing experimental datasets and innovative context- specific assumptions. I produced an extensive survey of the modeling efforts previously applied for studying metabolism of P. falciparum and available large-scale experimental datasets in comparison with the similar efforts made in other species. Further, I curated an existing model of metabolism in Plasmodium falciparum with respect to an up-to-date primary literature on metabolism of the parasite and addressed several important assumptions implicitly made in this model. Using a state-of-the-art approach, I reconstructed de novo a comprehensive metabolic model of T. gondii, and performed an extensive computational analysis to explore its metabolic needs and capabilities. I identified and classified the minimal set of substrates the parasite utilizes for growth, along with the genes and pairs of genes that are essential for cellular functions such as growth and energy metabolism. Subsequently, several of the model-driven hypotheses were confirmed experimentally, while for validation of the majority of the computational predictions forthcoming high-throughput approaches shall be used. Every confirmed hypothesis expands the scope of our knowledge on peculiarities of metabolism in apicomplexan parasites and hence can serve as an input for the pipeline of developing novel medicines.

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

Meriç Ataman, Vassily Hatzimanikatis, Dominique Soldati-Favre, Stepan Tymoshenko

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.

Public Library of Science2017

Refining Regulatory Networks through Phylogenetic Transfer of Information

Bernard Moret, Xiuwei Zhang

The experimental determination of transcriptional regulatory networks in the laboratory remains difficult and time-consuming, while computational methods to infer these networks provide only modest accuracy. The latter can be attributed partly to the limitations of a single-organism approach. Computational biology has long used comparative and evolutionary approaches to extend the reach and accuracy of its analyses. In this paper, we describe ProPhyC, a probabilistic phylogenetic model and associated inference algorithms, designed to improve the inference of regulatory networks for a family of organisms by using known evolutionary relationships among these organisms. ProPhyC can be used with various network evolutionary models and any existing inference method. Extensive experimental results on both biological and synthetic data confirm that our model (through its associated refinement algorithms) yields substantial improvement in the quality of inferred networks over all current methods. We also compare ProPhyC with a transfer learning approach we design. This approach also uses phylogenetic relationships while inferring regulatory networks for a family of organisms. Using similar input information but designed in a very different framework, this transfer learning approach does not perform better than ProPhyC, which indicates that ProPhyC makes good use of the evolutionary information.

2012

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.