Metabolomics, lipidomics and proteomics profiling of myoblasts infected with Trypanosoma cruzi after treatment with different drugs against Chagas disease

Introduction Chagas disease, the most important parasitic infection in Latin America, is caused by the intracellular protozoan Trypanosoma cruzi . To treat this disease, only two nitroheterocyclic compounds with toxic side effects exist and frequent treatment failures are reported. Hence there is an urgent need to develop new drugs. Recently, metabolomics has become an efficient and cost-effective strategy for dissecting drug mode of action, which has been applied to bacteria as well as parasites, such as different Trypanosome species and forms. Objectives We assessed if the metabolomics approach can be applied to study drug action of the intracellular amastigote form of T. cruzi in a parasite-host cell system. Methods We applied a metabolic fingerprinting approach (DI-MS and NMR) to evaluate metabolic changes induced by six different (candidate) drugs in a parasite-host cell system. In a second part of our study, we analyzed the impact of two drugs on polar metabolites, lipid and proteins to evaluate if affected pathways can be identified. Results Metabolic signatures, obtained by the fingerprinting approach, resulted in three different clusters. Two can be explained by already known of mode actions, whereas the three experimental drugs formed a separate cluster. Significant changes induced by drug action were observed in all the three metabolic fractions (polar metabolites, lipids and proteins). We identified a general impact on the TCA cycle, but no specific pathways could be attributed to drug action, which might be caused by a high percentage of common metabolome between a eukaryotic host cell and a eukaryotic parasite. Additionally, ion suppression effects due to differences in abundance between host cells and parasites may have occurred. Conclusion We validated the metabolic fingerprinting approach to a complex host-cell parasite system. This technique can potentially be applied in the early stage of drug discovery and could help to prioritize early leads or reconfirmed hits for further development.