Potential Gut Microbiota Features for Non-Invasive Detection of Schistosomiasis

Abstract

The gut microbiota has been identified as a predictive biomarker for various diseases. However, few studies focused on the diagnostic accuracy of gut microbiota derived-signature for predicting hepatic injuries in schistosomiasis. Here, we characterized the gut microbiomes from 94 human and mouse stool samples using 16S rRNA gene sequencing. The diversity and composition of gut microbiomes in Schistosoma japonicum infection-induced disease changed significantly. Gut microbes, such as Bacteroides, Blautia, Enterococcus, Alloprevotella, Parabacteroides and Mucispirillum, showed a significant correlation with the level of hepatic granuloma, fibrosis, hydroxyproline, ALT or AST in S. japonicum infection-induced disease. We identified a range of gut bacterial features to distinguish schistosomiasis from hepatic injuries using the random forest classifier model, LEfSe and STAMP analysis. Significant features Bacteroides, Blautia, and Enterococcus and their combinations have a robust predictive accuracy (AUC: from 0.8182 to 0.9639) for detecting liver injuries induced by S. japonicum infection in humans and mice. Our study revealed associations between gut microbiota features and physiopathology and serological shifts of schistosomiasis and provided preliminary evidence for novel gut microbiota-derived features for the non-invasive detection of schistosomiasis.