AUTHOR=Lin Datao , Song Qiuyue , Liu Jiahua , Chen Fang , Zhang Yishu , Wu Zhongdao , Sun Xi , Wu Xiaoying 

TITLE=Potential Gut Microbiota Features for Non-Invasive Detection of Schistosomiasis

JOURNAL=Frontiers in Immunology

VOLUME=13

YEAR=2022

URL=https://www.frontiersin.org/journals/immunology/articles/10.3389/fimmu.2022.941530

DOI=10.3389/fimmu.2022.941530

ISSN=1664-3224

ABSTRACT=<p>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 <italic>Schistosoma japonicum</italic> infection-induced disease changed significantly. Gut microbes, such as <italic>Bacteroides</italic>, <italic>Blautia</italic>, <italic>Enterococcus, Alloprevotella</italic>, <italic>Parabacteroides</italic> and <italic>Mucispirillum</italic>, showed a significant correlation with the level of hepatic granuloma, fibrosis, hydroxyproline, ALT or AST in <italic>S. japonicum</italic> 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 <italic>Bacteroides</italic>, <italic>Blautia</italic>, and <italic>Enterococcus</italic> and their combinations have a robust predictive accuracy (AUC: from 0.8182 to 0.9639) for detecting liver injuries induced by <italic>S. japonicum</italic> 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.</p>