UHPLC-MS-Based Metabolomics Analysis Reveals the Process of Schistosomiasis in Mice.

Abstract

Metabolomics, as an emerging technology, has been demonstrated to be a very powerful tool in the study of the host metabolic responses to infections by parasites. Schistosomiasis is a parasitic infection caused by schistosoma worm via the direct contact with the water containing cercaria, among which Schistosoma japonicum (S. japonicum) is endemic in Asia. In order to characterize the schistosome-induced changes in the host metabolism and further to develop the strategy for early diagnosis of schistosomiasis, we performed comprehensive LC-MS-based metabolomics analysis of serum from mice infected by S. japonicum for 5 weeks. With the developed diagnosis strategy based on our metabolomics data, we were able to successfully detect schistosomiasis at the first week post-infection, which was 3 weeks earlier than "gold standard" methods and 2 weeks earlier than the methods based on 1H NMR spectroscopy. Our metabolomics study revealed that S. japonicum infection induced the metabolic changes involved in a variety of metabolic pathways including amino acid metabolism, DNA and RNA biosynthesis, phospholipid metabolism, depression of energy metabolism, glucose uptake and metabolism, and disruption of gut microbiota metabolism. In addition, we identified seventeen specific metabolites whose down-regulated profiles were closely correlated with the time-course of schistosomiasis progression and can also be used as an indicator for the worm-burdens. Interestingly, the decrease of these seventeen metabolites was particularly remarkable at the first week post-infection. Thus, our findings on mechanisms of host-parasite interaction during the disease process pave the way for the development of an early diagnosis tool and provide more insightful understandings of the potential metabolic process associated with schistosomiasis in mice. Furthermore, the diagnosis strategy developed in this work is cost-effective and is superior to other currently used diagnosis methods.

• Authors: Yuzheng Huang, Qiong Wu, Liang Zhao, Chunrong Xiong, Yongliang Xu, Xin Dong, Yan Wen, Jun Cao
• Journal: Frontiers in microbiology (Front Microbiol) Vol. 11 Pg. 1517 ( 2020) ISSN: 1664-302X [Print] Switzerland
• PMID: 32760365 (Publication Type: Journal Article)
• Copyright: Copyright © 2020 Huang, Wu, Zhao, Xiong, Xu, Dong, Wen and Cao.