Forensic analysis of the microbiome of phones and shoes

被引:123
作者
Lax, Simon [1 ,2 ]
Hampton-Marcell, Jarrad T. [1 ]
Gibbons, Sean M. [1 ,3 ]
Colares, Georgia Barguil [4 ]
Smith, Daniel [1 ,5 ]
Eisen, Jonathan A. [6 ,7 ,8 ]
Gilbert, Jack A. [1 ,2 ,3 ,9 ,10 ]
机构
[1] Argonne Natl Lab, Dept Biosci, Inst Genom & Syst Biol, Argonne, IL 60439 USA
[2] Univ Chicago, Dept Ecol & Evolut, Chicago, IL 60637 USA
[3] Univ Chicago, Grad Program Biophys Sci, Chicago, IL 60637 USA
[4] Univ Fed Ceara, Dept Biol, BR-60440900 Fortaleza, Ceara, Brazil
[5] Baylor Coll Med, Alkek Ctr Metagen & Microbiome Res, Dept Mol Virol & Microbiol, Houston, TX 77030 USA
[6] Univ Calif Davis, Dept Ecol & Evolut, Davis, CA 95616 USA
[7] Univ Calif Davis, Dept Med Microbiol & Immunol, Davis, CA 95616 USA
[8] Univ Calif Davis, UC Davis Genome Ctr, Davis, CA 95616 USA
[9] Marine Biol Lab, Woods Hole, MA 02543 USA
[10] Zhejiang Univ, Coll Environm & Resource Sci, Hangzhou 310058, Zhejiang, Peoples R China
基金
美国国家卫生研究院;
关键词
Forensic microbiology; Source-sink dynamics; Shoe microbiome; Phone microbiome; Microbial time series; GUT;
D O I
10.1186/s40168-015-0082-9
中图分类号
Q93 [微生物学];
学科分类号
071005 ; 100705 ;
摘要
Background: Microbial interaction between human-associated objects and the environments we inhabit may have forensic implications, and the extent to which microbes are shared between individuals inhabiting the same space may be relevant to human health and disease transmission. In this study, two participants sampled the front and back of their cell phones, four different locations on the soles of their shoes, and the floor beneath them every waking hour over a 2-day period. A further 89 participants took individual samples of their shoes and phones at three different scientific conferences. Results: Samples taken from different surface types maintained significantly different microbial community structures. The impact of the floor microbial community on that of the shoe environments was strong and immediate, as evidenced by Procrustes analysis of shoe replicates and significant correlation between shoe and floor samples taken at the same time point. Supervised learning was highly effective at determining which participant had taken a given shoe or phone sample, and a Bayesian method was able to determine which participant had taken each shoe sample based entirely on its similarity to the floor samples. Both shoe and phone samples taken by conference participants clustered into distinct groups based on location, though much more so when an unweighted distance metric was used, suggesting sharing of low-abundance microbial taxa between individuals inhabiting the same space. Conclusions: Correlations between microbial community sources and sinks allow for inference of the interactions between humans and their environment.
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页数:8
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