Metabolomics reveals membrane lipids, aspartate/asparagine and nucleotide metabolism pathway differences associated with chloroquine resistance in Plasmodium vivax malaria

Background: Chloroquine (CQ) is the main anti-schizontocidal drug used in the treatment of uncomplicated malaria caused by Plasmodium vivax. Chloroquine resistant P. vivax (PvCR) malaria in the Western Pacific region, Asia and in the Americas indicates a need for biomarkers of resistance to improve therapy and enhance understanding of the mechanisms associated with PvCR. In this study, we compared metabolic profiles of PvCR and chloroquine sensitive P. vivax malaria patients before treatment to identify potential molecular markers of chloroquine resistance. In addition, network analysis was performed to identify metabolic features and pathways associated with different clinical laboratory measurements such as parasitemia, leucocytes, platelets, gametocytes, hemoglobin, and other clinical metadata on these patients. Materials and Methods: An untargeted high-resolution metabolomics (LC-MS) analysis was performed on plasma samples collected in a malaria clinic in Manaus, Brazil. Male and female patients with Plasmodium vivax were included (n=46); samples were collected before CQ treatment and followed for 28 days to determine PvCR, defined as the recurrence of parasitemia with detectable plasma concentrations of CQ ≥100 ng/dL. Differentially expressed metabolic features between CQ-Resistant (CQ-R) and CQ-Sensitive (CQ-S) patients were identified using partial least squares discriminant analysis (PLS-DA) and linear regression after adjusting for 3 covariates and multiple testing correction. Pathway enrichment analysis was performed using Mummichog. Network analysis was performed using xMWAS. Results and Conclusions: Linear regression and PLS-DA methods yielded 69 discriminatory features between CQ-R and CQ-S groups, with 10-fold cross-validation classification accuracy of 89.6% using a support vector machine classifier. Pathway enrichment analysis showed significant enrichment (p<0.05) of glycerophospholipid metabolism, glycosphingolipid metabolism, aspartate and asparagine metabolism, purine and pyrimidine metabolism, and xenobiotics metabolism. Glycerophosphocholines levels were significantly lower in the CQ-R group as compared to CQ-S patients and also to independent control samples. Network analysis allowed visualization and identification of common and unique associations between different clinical variables and metabolic features. The results show that high-resolution metabolomics is promising for the development of new tools to understand the biological process and to identify potential biomarkers of PvCR.