Forensic analysis of the microbiome of phones and shoes

dc.contributor.author Lax, Simon
dc.contributor.author Hampton-Marcell, Jarrad T.
dc.contributor.author Gibbons, Sean M.
dc.contributor.author Colares, Georgia Barguil
dc.contributor.author Smith, Daniel
dc.contributor.author Eisen, Jonathan A.
dc.contributor.author Gilbert, Jack A.
dc.date.accessioned 2015-05-19T14:05:10Z
dc.date.available 2015-05-19T14:05:10Z
dc.date.issued 2015-05-12
dc.description © The Author(s), 2015. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Microbiome 3 (2015): 21, doi:10.1186/s40168-015-0082-9. en_US
dc.description.abstract 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. 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. Correlations between microbial community sources and sinks allow for inference of the interactions between humans and their environment. en_US
dc.description.sponsorship This work was enabled by the generous support of the Alfred P Sloan foundation. This work was supported in part by the U.S. Dept. of Energy under Contract DE-AC02-06CH11357. S.M.G. was supported by an EPA STAR Graduate Fellowship and by a National Institutes of Health Training Grant 5 T-32 EB-009412. en_US
dc.format.mimetype application/pdf
dc.format.mimetype application/vnd.ms-excel
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dc.identifier.citation Microbiome 3 (2015): 21 en_US
dc.identifier.doi 10.1186/s40168-015-0082-9
dc.identifier.uri https://hdl.handle.net/1912/7295
dc.language.iso en_US en_US
dc.publisher BioMed Central en_US
dc.relation.uri https://doi.org/10.1186/s40168-015-0082-9
dc.rights Attribution 4.0 International *
dc.rights.uri http://creativecommons.org/licenses/by/4.0/ *
dc.subject Forensic microbiology en_US
dc.subject Source-sink dynamics en_US
dc.subject Shoe microbiome en_US
dc.subject Phone microbiome en_US
dc.subject Microbial time series en_US
dc.title Forensic analysis of the microbiome of phones and shoes en_US
dc.type Article en_US
dspace.entity.type Publication
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