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Integration of gut microbiome, host biomarkers, and urine metabolome data reveals networks of interactions associated with distinct clinical phenotypes

By Rui-Jun Li, Zhu-Ye Jie, Qiang Feng, Rui-Ling Fang, Fei Li, Yuan Gao, Hui-Hua Xia, Huan-Zi Zhong, Bin Tong, Jia-Yu Xu, Lise Madsen, Chun-Lei Liu, Zhen-Guo Xu, Jian Wang, Huan-Ming Yang, Xun Xu, Yong Hou, Susanne Brix, Karsten Kristiansen, RXin-Lei Yu, Hui-Jue Jia, Kun-Lun He

Posted 02 Jan 2019
bioRxiv DOI: 10.1101/509703

Background: Comprehensive analyses of multi-omics data may provide insights into interactions between different biological layers in relation to distinct clinical features. Here we integrated data on the gut microbiota, blood parameters and urine metabolites of treatment-naive individuals presenting a wide range of metabolic disease phenotypes to delineate clinically meaningful associations. Results: Trans-omics correlation networks revealed that candidate gut microbial biomarkers and urine metabolite features covaried with distinct clinical phenotypes. Integration of the gut microbiome, the urine metabolome and the phenome revealed that variations in one of these three systems correlated with changes in the other two. Of specific note in relation to clinical parameters of liver function, we identified Eubacterium eligens, Faecalibacterium prausnitzii and Ruminococcus lactaris to be associated with a healthy liver function, whereas Clostridium bolteae, Tyzzerella nexills, Ruminococcus gnavus, Blautia hansenii, and Atopobium parvulum were associated with blood biomarkers for liver diseases. Variations in these microbiota features paralleled changes in specific urine metabolites. Network modeling yielded two core clusters including one large gut microbe-urine metabolite close-knit cluster and one triangular cluster composed of a gut microbe-blood-urine network, demonstrating close inter-system crosstalk especially between the gut microbiome and the urine metabolome. Conclusions: Distinct clinical phenotypes are manifested in both the gut microbiome and the urine metabolome, and inter-domain connectivity takes the form of high-dimensional networks. Such networks may further our understanding of complex biological systems, and may provide a basis for identifying biomarkers for diseases.

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