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submitted on 14.07.2020 and posted on 14.07.2020by Shunsuke Tomita, Hiroyuki Kusada, Naoshi Kojima, Sayaka Ishihara, Koyomi Miyazaki, Hideyuki Tamaki, Ryoji Kurita
Understanding the status of gut microbiota has been
recognized as crucial in health management and disease treatment. To meet the
demands of medical and biological applications where rapid evaluation of gut
microbiota in limited research environment is essential, we developed new sensing
systems able to readout the overall characteristics of complex microbiota. Response
patterns generated by a synthetic library of 12 charged block-copolymers with aggregation-induced
emission units were analyzed with pattern recognition algorithms, allowing to
identify the species/phyla of 16 axenic cultures of intestinal bacterial
strains. More importantly, our method clearly classified artificial models of
obesity-associated gut microbiota, and further succeeded in detecting sleep
disorders in mice through comparative analysis of the normal/abnormal mouse gut
microbiota. Our techniques can analyze complex bacterial samples far more
quickly, simply and inexpensively than common metagenome-based methods, offering
a powerful and complementary tool for gut microbiome analysis for practical
use, e.g., in clinical settings.