Metabolic Needs and Capabilities of Toxoplasma gondii through Combined Computational and Experimental Analysis
Journal article, 2015

Toxoplasma gondii is a human pathogen prevalent worldwide that poses a challenging and unmet need for novel treatment of toxoplasmosis. Using a semi-automated reconstruction algorithm, we reconstructed a genome-scale metabolic model, ToxoNet1. The reconstruction process and flux-balance analysis of the model offer a systematic overview of the metabolic capabilities of this parasite. Using ToxoNet1 we have identified significant gaps in the current knowledge of Toxoplasma metabolic pathways and have clarified its minimal nutritional requirements for replication. By probing the model via metabolic tasks, we have further defined sets of alternative precursors necessary for parasite growth. Within a human host cell environment, ToxoNet1 predicts a minimal set of 53 enzyme-coding genes and 76 reactions to be essential for parasite replication. Double-gene-essentiality analysis identified 20 pairs of genes for which simultaneous deletion is deleterious. To validate several predictions of ToxoNet1 we have performed experimental analyses of cytosolic acetyl-CoA biosynthesis. ATP-citrate lyase and acetyl-CoA synthase were localised and their corresponding genes disrupted, establishing that each of these enzymes is dispensable for the growth of T. gondii, however together they make a synthetic lethal pair.


S. Tymoshenko

Ecole Polytechnique Federale De Lausanne

Swiss Institute of Bioinformatics

University of Geneva

R.D. Oppenheim

University of Geneva

Rasmus Ågren

Chalmers, Biology and Biological Engineering, Systems and Synthetic Biology

Jens B Nielsen

Chalmers, Biology and Biological Engineering, Systems and Synthetic Biology

D. Soldati-Favre

University of Geneva

V. Hatzimanikatis

University of Geneva

Swiss Institute of Bioinformatics

PLoS Computational Biology

1553-734X (ISSN) 1553-7358 (eISSN)

Vol. 11 5 Art. no. e1004261- e1004261

Areas of Advance

Life Science Engineering (2010-2018)

Subject Categories

Bioinformatics and Systems Biology





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