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Despite immense efforts to combat malaria in tropical and sub-tropical regions, the potency of this vector-borne disease and its status as a major driver of morbidity and mortality remain undisputed. We develop an analytical pipeline for characterizing  Plasmodium  infection in a mouse model and identify candidate urinary biomarkers that may present alternatives to immune-based diagnostic tools. We employ  1 H nuclear magnetic resonance (NMR) profiling followed by multivariate modeling to discover diagnostic spectral regions. Identification of chemical structures is then made on the basis of statistical spectroscopy, multinuclear NMR, and entrapment of candidates by iterative liquid chromatography (LC) and mass spectrometry (MS). We identify two urinary metabolites (i) 4-amino-1-[3-hydroxy-5-(hydroxymethyl)-2,3-dihydrofuran-2-yl]pyrimidin-2(1H)-one, (ii) 2-amino-4-({[5-(4-amino-2-oxopyrimidin-1(2H)-yl)-4-hydroxy-4,5-dihydrofuran-2-yl]methyl}sulfanyl)butanoic acid that were detected only in  Plasmodium berghei -infected mice. These metabolites have not been described in the mammalian or parasite metabolism to date. This analytical pipeline could be employed in prospecting for infection biomarkers in human populations.

Metabolic Profiling Framework for Discovery of Candidate Diagnostic Markers of Malaria