image: Computational analysis of the malaria parasites' metabolism aids in the understanding of observed phenotypes. Red fluorescent Plasmodium berghei parasites infect green fluorescent erythrocytes of mouse. view more
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 in the EPFL generated the final image.
A new mathematical model, based on the deadliest malaria parasite, Plasmodium falciparum, could help develop antimalarials by identifying key metabolic targets, according to a study published in PLOS Computational Biology by Vassily Hatzimanikatis at École Polytechnique Fédérale de Lausanne (EPFL), Switzerland, and colleagues.
Malaria, a mosquito-borne infectious disease caused by parasites of the Plasmodium genus, is becoming increasingly difficult to treat as the parasites develop resistance to current drugs. A promising new strategy is to target the parasites' metabolism, but it has proven to be both versatile and complex, making it difficult to target. It has also been difficult to integrate existing experimental data on the metabolism with genetic data on the genome sequence, gene expression, and essential genes for growth.
To overcome these obstacles, the authors of the present study developed a model that accurately connects experimental information from both genetics and metabolomics. They looked at the thermodynamic properties of the metabolic reactions, which relate to the way the parasites use and produce energy. This focus on the energetics of the reactions allows them to analyse, for the first time, which metabolic functions are thermodynamically coupled and are essential during infection. It reveals complex interactions between the parasites' genes and metabolism, which, the authors state, could identify potential mechanisms to target with drugs.
"The model integrates all available knowledge on the genetics and metabolism of the parasites and allows the formulation of testable hypotheses behind the parasite's essential functions," says Dr. Hatzimanikatis. "Ultimately, it can accelerate the discovery toward novel antimalarial drug targets."
The EPFL scientists will now continue to calibrate and improve the predictive capabilities of the model with additional genetics and metabolomics data provided by collaborators from the MalarX.ch consortium in the University of Geneva and Bern and the Wellcome Trust Sanger Institute. They hope to reveal the mechanisms behind host-pathogen interactions and gain insight into the physiology of the parasite while it is dormant.
This press release is based on text provided by the authors.
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In your coverage please use this URL to provide access to the freely available article in PLOS Computational Biology: http://journals.plos.org/ploscompbiol/article?id=10.1371/journal.pcbi.1005397
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.
Competing Interests: The authors have declared that no competing interests exist.
Journal
PLoS Computational Biology