# Application of a Non-Targeted Metabolomics Study in Plasmodium berghei-Infected Rats: Towards Unravelling Metabolic Alterations During Malaria Infection

## TL;DR

This study uses metabolomics to identify metabolic changes in rats infected with Plasmodium berghei, revealing potential biomarkers for malaria.

## Contribution

The study applies non-targeted metabolomics to uncover specific metabolic alterations in malaria-infected rats.

## Key findings

- Infected rats showed elevated urea levels and reduced 1,5-anhydroglucitol, D-(+)-Talose, and arachidonic acid.
- Pathways like the glucose-alanine cycle and alpha-linolenic acid metabolism were significantly enriched in infected rats.
- Metabolomics shows potential as a diagnostic tool for malaria detection and prognosis.

## Abstract

Falciparum malaria is a life-threatening vector-borne disease prevalent in tropical and subtropical regions. The complexity of severe malaria demands a thorough investigation of host–parasite interactions. Twenty male Sprague Dawley rats were divided into two groups: uninfected controls and Plasmodium berghei-infected rats, infected via intraperitoneal injection of parasitized red blood cells. Serum samples were analysed using high-resolution untargeted Gas Chromatography–Time-of-Flight Mass Spectrometry. Metabolomic analyses revealed altered metabolites and enriched metabolic pathways. Distinct metabolite profiles were observed between infected and control groups. Infected rats showed elevated urea levels and reduced concentrations of 1,5-anhydroglucitol, D-(+)-Talose, and arachidonic acid. Pathway analysis revealed significant enrichment of the glucose-alanine cycle, alpha-linolenic acid metabolism, and linoleic acid metabolism in infected rats. Minimal enrichment was observed in arachidonic acid metabolism and lactose biosynthesis. The upregulation of the glucose-alanine cycle suggests increased gluconeogenesis in response to parasite-induced glucose depletion and energy demand. Elevated urea indicates enhanced amino acid catabolism. These findings highlight the potential of metabolomics as a diagnostic tool for malaria detection and prognosis.