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

Abstract

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 plasma metabolic profiles of P. vivax malaria patients with PvCR and chloroquine sensitive parasites before treatment to identify potential molecular markers of chloroquine resistance.

Methods: An untargeted high-resolution metabolomics 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 and linear regression after adjusting for covariates and multiple testing correction. Pathway enrichment analysis was performed using Mummichog.

Results: 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 SVM 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.

Conclusions: The results show differences in lipid, amino acids, and nucleotide metabolism pathways in the plasma of CQ-R versus CQ-S patients prior to antimalarial treatment. Metabolomics phenotyping of P. vivax samples from patients with well-defined clinical CQ-resistance is promising for the development of new tools to understand the biological process and to identify potential biomarkers of PvCR.

Fig 1. Study design for identifying host metabolite factors that are associated with chloroquine resistance.

Schematic depicts timeline of sample collection and processing. Individuals with P. vivax malaria were enrolled at Day 0 and followed for 42 days. At enrollment, plasma was collected and stored for later processing by high-resolution metabolomics, and parasite strain was determined. Individuals then treated with chloroquine for three consecutive days and were monitored at Days 0, 1, 2, 3, 7, 14, 28 and 42 with complete blood count (CBC) and parasite count to determine if parasites recurred following CQ-treatment. CQ-Resistance (CQ-R) was assessed based on presence of the same strain parasite during the recurrence, along with high levels of CQ and DSQ in the bloodstream, as assessed by HPLC. Metabolomics analysis was performed to compare differences in host metabolites prior to CQ-treatment, comparing individuals who developed CQ-R (N = 15) versus CQ-Sensitive (CQ-S) who did not (N = 31).

Fig 2. Identification of metabolic features associated with CQ resistance.

A) Type 1 Manhattan plot, -log₁₀ p vs mass-to-charge. 81 m/z features with a broad range of m/z were found significant at FDR 0.20 threshold. Green dots represent the features that were up-regulated in the CQ-Resistant group and the red dots represent the features that were higher in the CQ-Sensitive group; B) Type 2 Manhattan plot, -log₁₀ p vs retention time, Majority of features had retention time greater than 4 minutes, which is consistent with elution profile of lipids on a C18 column; C) Two-way hierarchical clustering analysis using discriminatory features; D) Mummichog enriched pathways.

Fig 3. Comparison of glycerophosphocholine abundance levels in NIST, pooled normal plasma (US), healthy controls (Brazil), CQ-Resistant (P. vivax), and CQ-Sensitive (P. vivax) groups along with 95% confidence intervals.

The glycerophosphocholines were found to be lower in CQ-Resistant group as compared to CQ-Sensitive and other control samples (p<0.05).

Fig 4. 10-fold cross-validation analysis using clinical variables and top discriminatory metabolic features.

10-fold cross-validation classification accuracies varied from 65% to 89.6% using platelet count, glycerophosphocholines, top 10, top 30, and all 69 discriminatory features. The average permuted accuracies (N = 1000 permutations) varied from 55–58%.