Fighting malaria with math
A mathematic model of the deadliest malaria parasite, Plasmodium falciparum, could help science develop drugs to wipe out its' existence in the mosquito.
Malaria is a mosquito-borne infectious disease caused by parasites, and it is becoming increasingly difficult to treat. The parasites have developed resistance to current drugs on the market. A promising new strategy is to target their metabolism. Although a reasonable solution — it is complex. Integrating existing data on parasite metabolism to its genome, relevant genes and their expression/function, as well as identifying its growth genes, all are part of the solution — and difficult to do.
To overcome enormous obstacles, the present study developed a math solution connecting experimental information from both genetics and metabolomics — the study of chemical fingerprints cell processes leave behind.
The results are published in PLOS Computational Biology by Vassily Hatzimanikatis PhD, from the École Polytechnique Fédérale de Lausanne (EPFL) Switzerland, along with his colleagues.
They measured the thermodynamics parasites' use to produce the energy their bodies need to function. This allows other researchers to analyse which metabolic functions are thermodynamically joined with infecting a host. Their results reveal the complex interactions between parasite genes and metabolism, and could help isolate targets for potential drugs.
"The model integrates all available knowledge on genetics and metabolism of the parasites, allowing for formation of a testable hypotheses behind the parasite's essential functions. Ultimately, it can accelerate discovery of antimalarial drug targets."
Vassily Hatzimanikatis PhD, Laboratory of Computational Systems Biotechnology, École Polytechnique Fédérale de Lausanne [EPFL], Switzerland.
EPFL scientists continue to calibrate and improve the capability of this new computation model using additional genetics and data from collaborators at the MalarX consortium, University of Geneva and Bern, along with collaborators from the Wellcome Trust Sanger Institute. Their hope is to distinguish metabolic functions behind the host-pathogen relationship and determine when the parasite is dormant and when it is not.
Some malaria parasite species can go dormant and survive for years in host liver cells before becoming active again. In order to eradicate such parasites, scientists want to target that dormant stage.
Currently, only the drug “Primaquine” can eliminate liver stage parasites. But, it has shortcomings, including a two-week daily regimen. Also, severe side effects in people with Glucose-6-phosphate dehydrogenase (G6PD) deficiency. G6PD deficiency is an X-linked recessive gene error that mostly predisposes men, to a spontaneous destruction of their red blood cells and severe jaundice (hemolysis). A number of triggers, such as certain foods, illness, and even medication can trigger a G6PD response.
As dormant liver stage parasites can take months or even years to activate — they aren't easily studied in the lab. Although monkey models of the dormant liver stage exist, experiments in living organisms don't allow for large-scale drug screenings. The situation calls for a programmable model.
The ability to create a mathematic model of all parts of a parasite's metabolism will greatly advance our control of their life cycle, and possible death.
Novel antimalarial therapies are urgently needed for the fight against drug-resistant parasites. 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 thermodynamics-based flux analysis (TFA). Using previous absolute concentration data of the intraerythrocytic 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. falciparum 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.
Almost half of the world population is at risk of infection by malaria parasites. The rise in drug-resistant parasites requires better understanding and targeting of their metabolism. In this study, we present a genome-scale metabolic reconstruction (iPfa) of the deadliest malaria parasite, Plasmodium falciparum, and its thermodynamics-based flux analysis (TFA). Our results support and extend the available experimental evidence on the essential genes and nutritional requirements of this organism. Besides, we identify metabolites that give rise to thermodynamic bottlenecks and suggest substrate channeling. Overall, these results provide novel insight into the metabolism of P. falciparum and may guide experimental studies to develop a better characterization of the parasite metabolism and the identification of antimalarial drug targets.
Competing Interests: The authors have declared that no competing interests exist.
Citation: Chiappino-Pepe A, Tymoshenko S, Ataman M, Soldati-Favre D, Hatzimanikatis V (2017) Bioenergetics-based modeling of Plasmodium falciparum metabolism reveals its essential genes, nutritional requirements, and thermodynamic bottlenecks. PLoS Comput Biol 13 (3): e1005397. doi:10.1371/journal.pcbi.1005397
Funding: VH, DSF, ACP, ST, and MA are supported by the RTD grants MalarX and MicroScapesX within SystemsX.ch, the Swiss Initiative for Systems Biology evaluated by the Swiss National Science Foundation: http://www.systemsx.ch/index.php?id=276&L=3 and http://www.systemsx.ch/index.php?id=277&L=3. VH, ACP, ST and MA are supported by the Ecole Polytechnique Federale de Lausanne. DSF is supported by the University of Geneva. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
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Apr 14, 2017 Fetal Timeline Maternal Timeline News News Archive
Red fluorescent Plasmodium berghei parasites infect green fluorescent mouse erythrocytes.
Computational analysis of malaria parasites' metabolism helps us understand the interaction of its genotype with its environment. The method quickly calculates individual immune responses.
Image Credit: Prof. Volker Heussler in the University of Bern produced the images of
fluorescent malaria parasites. Dr. Soh and Dr. Ljubisa in the EPFL generated the metabolic
network. Prof. Vassily Hatzimanikatis and Anush Chiappino-Pepe generated the final image.