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Polymer-Based Chemical Nose Systems for Optical Pattern Recognition of Gut Microbiota

preprint
submitted on 14.07.2020 and posted on 14.07.2020 by 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.

Funding

JSPS KAKENHI grant (JP17H04884 and JP20H02774 to S.T.; JP19H05679 and JP19H05683 (Post- Koch Ecology) to H.K. and H.T.)

AMED PRIME (Grant number JP18gm6010019) to H.T.

JST ERATO Grant Number JPMJER1502 to H.T.

DAICENTER project grant from the DBT (Govt. of India) to Renu Wadhwa and special strategic grant from AIST (Japan) to R.K.

History

Email Address of Submitting Author

s.tomita@aist.go.jp

Institution

National Institute of Advanced Industrial Science and Technology

Country

Japan

ORCID For Submitting Author

0000-0002-0586-6976

Declaration of Conflict of Interest

The authors declare no competing interest.

Version Notes

First version

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