Characterizing the Personalized Microbiota Dynamics for Disease Classification by Individual-Specific Edge-Network Analysis - Yu, Xiangtian; Chen, Xiaoyu; Wang, Zhenjia (Frontiers in Genetics, 2019)
Environmental factors such as gut microbiome are thought to play an important role in many disease development and treatments. But our understanding of microbiota compositional dynamics is still unclear and incomplete because the intestinal microbial community is an easily-changed ecosystem. It is urgently required to overcome the large variations among individuals, meanwhile these individual varying information will be an asset rather than a limitation to the personalized medicine. For a proof-of-concept study on the individual-specific disease classification based on microbiota compositional dynamics, we carried on an adjusted individual-specific edge-network analysis (iENA) method to analyze the temporal 16S rRNA (ribosomal RNA) gene sequencing data from individuals in a challenge study. Our identified individual-specific OTU markers or their combined makers have good consistence with previously reported markers, and the predictive score based on them can perform a better AUROC than previous 0.83 and achieve about 90% accuracy on predicting whether an individual developed diarrhea/ symptomatic or not. All these results suggest that the combination of high-throughput microbiome experiment and computational system biology approaches can efficiently recommend potential candidate species in the defense against various pathogens for precision medicine.