Untargeted serum metabolomic profiling for early detection of Schistosoma mekongi infection in mouse model
Issued Date
2022-08-18
Resource Type
eISSN
22352988
Scopus ID
2-s2.0-85137241267
Pubmed ID
36061860
Journal Title
Frontiers in Cellular and Infection Microbiology
Volume
12
Rights Holder(s)
SCOPUS
Bibliographic Citation
Frontiers in Cellular and Infection Microbiology Vol.12 (2022)
Suggested Citation
Chienwichai P., Nogrado K., Tipthara P., Tarning J., Limpanont Y., Chusongsang P., Chusongsang Y., Tanasarnprasert K., Adisakwattana P., Reamtong O. Untargeted serum metabolomic profiling for early detection of Schistosoma mekongi infection in mouse model. Frontiers in Cellular and Infection Microbiology Vol.12 (2022). doi:10.3389/fcimb.2022.910177 Retrieved from: https://repository.li.mahidol.ac.th/handle/123456789/85629
Title
Untargeted serum metabolomic profiling for early detection of Schistosoma mekongi infection in mouse model
Other Contributor(s)
Abstract
Mekong schistosomiasis is a parasitic disease caused by blood flukes in the Lao People’s Democratic Republic and in Cambodia. The standard method for diagnosis of schistosomiasis is detection of parasite eggs from patient samples. However, this method is not sufficient to detect asymptomatic patients, low egg numbers, or early infection. Therefore, diagnostic methods with higher sensitivity at the early stage of the disease are needed to fill this gap. The aim of this study was to identify potential biomarkers of early schistosomiasis using an untargeted metabolomics approach. Serum of uninfected and S. mekongi-infected mice was collected at 2, 4, and 8 weeks post-infection. Samples were extracted for metabolites and analyzed with a liquid chromatography-tandem mass spectrometer. Metabolites were annotated with the MS-DIAL platform and analyzed with Metaboanalyst bioinformatic tools. Multivariate analysis distinguished between metabolites from the different experimental conditions. Biomarker screening was performed using three methods: correlation coefficient analysis; feature important detection with a random forest algorithm; and receiver operating characteristic (ROC) curve analysis. Three compounds were identified as potential biomarkers at the early stage of the disease: heptadecanoyl ethanolamide; picrotin; and theophylline. The levels of these three compounds changed significantly during early-stage infection, and therefore these molecules may be promising schistosomiasis markers. These findings may help to improve early diagnosis of schistosomiasis, thus reducing the burden on patients and limiting spread of the disease in endemic areas.