Research digital skills training 2021
Improving diagnosis for schistosomiasis by using the ‘metabolic footprint’ of urine samples from an animal model of Schistosoma infection to identify possible biomarkers
Rodrigo Loyo, Masters student, Brazil; Dr Augusto Barbosa, Supervisor, Biology Sciences; Dr Constança Simões Barbosa, Supervisor, Brazil; Dr Erica Zarate, Senior Technician GC-MS, Mass Spectrometry Centre
Figure 1. The world map showing the prevalence data of schistosomiasis in several countries.
Schistosomiasis is a neglected tropical disease caused by a trematode of the genus Schistosoma. The majority of human infections are attributable to the three species: S. haematobium, S. mansoni and S. japonicum (1). For S. mansoni, some eggs leave the body in the faeces and hatch in water to liberate miracidium larvae, which infect certain types of freshwater snails (2). Within the snail, the parasites multiply asexually to produce free-swimming cercariae larvae, and these then infect people by skin penetration (3). The adults do not multiply in the body but instead live there for several years, producing eggs (4).
Schistosomiasis infection constitutes a major public health problem, particularly in countries where the disease is endemic. Worldwide (Figure 1), it is estimated 779 million people at the risk of contracting schistosomiasis, while about 210 million are infected with the disease (5). The acute or short-term consequences of schistosomiasis infection include skin rashes, fever and fatigue, while chronic or long-term effects involve damage to internal organs such as the liver, spleen and gall bladder.
Traditionally, the schistosomiasis diagnosis has been performed by direct parasitological techniques, such as the Kato–Katz method (6). However, in cases of a low infections the Kato-Katz method is not efficient and may be detected by means of serological immunodiagnostic tests or molecular techniques (7–10). Alternative methods for the schistosomiasis diagnosis can potentially be identified through metabolomics studies, wherein the metabolites found associated with Schistosoma infections (e.g., in an animal’s blood, stool or urine) are profiled to identify characteristic biomarkers.
This study was a partnership between the Oswaldo Cruz Foundation (https://portal.fiocruz.br/pt-br) and the University of Auckland (UoA). The samples were obtained at the Aggeu Magalhães Institute (Brazil) and shipped to the UoA Mass Spectrometry Facility. The Mass Spectrometry facility provides the service to do metabolomics analysis for many researchers and students (Figure 2) around the University of Auckland. The GC-MS instruments and the data analysis computer are constantly in use. For this reason, this facility together with the Centre for eResearch created a virtual machine (VM) that enables a multi-user platform making the data processing easier and faster. This VM made possible a quickly way to work with all the required software and data generated by the equipment from everywhere and any computer to complete this metabolomics project.
Figure 2: Example of training session using the virtual mwachine in this Metabolomics analysis.
This study used the methyl chloroformate (MCF) methodology as described by Smart et al., 2010 (11) to do the metabolomics analysis. The samples were obtained from three groups of 5 mice (Figure 3). Two groups infected (low and high parasitological load) and one group not infected (used as a control).
The metabolic profile was different between groups with some metabolites contributing to these differences (Figure 4), like Hippuric acid and Alanine. The Hippuric acid was related to a low load infection as the Alanine was related to a high load infection (Figure 5).
Previous studies found similar results using different techniques (Wang et al., 2004, García-Pérez et al., 2008 and Li et al., 2011 (12–14)) showing the relevance of these metabolites to possible be used as biomarkers for schistosomiases diagnosis.
Figure 3. Result of a parasitological stool examination showing the difference between the groups.
Figure 4. Principal Component Analysis (PCA) showing the separation and the groups formation.
Figure 5. The linear regression showing a negative correlation between the Hippuric acid relative abundance and the parasitological load and a positive correlation between the Alanine relative abundance and the parasitological load.
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