Abstract
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 1H 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.
Highlights
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
To investigate the metabolic effects of a malaria-hookworm co-infection in the murine host in a laboratory-controlled experiment, and to identify candidate biomarkers that are predictive for P. berghei during single or coinfection, we randomly divided mice into five groups, (i) single infection with P. berghei; (ii) single infection with the mouse hookworm H. bakeri; (iii) simultaneous coinfection with both parasites; (iv) infection with H. bakeri prior to infection with P. berghei; and (v) uninfected (Fig. 1)
The experiment was run for 20 days in order to establish a chronic helminth infection prior to malaria coinfection
Summary
Metabolic Profiling Framework for Discovery of Candidate Diagnostic Markers of Malaria. Lucienne Tritten[1,2], Jennifer Keiser[1,2], Markus Godejohann[3], Jurg Utzinger[2,4], Mireille Vargas[1,2], Olaf Beckonert[5], Elaine Holmes5 & Jasmina Saric[5]
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