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      Forensic analysis of the microbiome of phones and shoes

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          Abstract

          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.

          Electronic supplementary material

          The online version of this article (doi:10.1186/s40168-015-0082-9) contains supplementary material, which is available to authorized users.

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          Most cited references 11

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          QIIME allows analysis of high-throughput community sequencing data.

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            UniFrac: a new phylogenetic method for comparing microbial communities.

            We introduce here a new method for computing differences between microbial communities based on phylogenetic information. This method, UniFrac, measures the phylogenetic distance between sets of taxa in a phylogenetic tree as the fraction of the branch length of the tree that leads to descendants from either one environment or the other, but not both. UniFrac can be used to determine whether communities are significantly different, to compare many communities simultaneously using clustering and ordination techniques, and to measure the relative contributions of different factors, such as chemistry and geography, to similarities between samples. We demonstrate the utility of UniFrac by applying it to published 16S rRNA gene libraries from cultured isolates and environmental clones of bacteria in marine sediment, water, and ice. Our results reveal that (i) cultured isolates from ice, water, and sediment resemble each other and environmental clone sequences from sea ice, but not environmental clone sequences from sediment and water; (ii) the geographical location does not correlate strongly with bacterial community differences in ice and sediment from the Arctic and Antarctic; and (iii) bacterial communities differ between terrestrially impacted seawater (whether polar or temperate) and warm oligotrophic seawater, whereas those in individual seawater samples are not more similar to each other than to those in sediment or ice samples. These results illustrate that UniFrac provides a new way of characterizing microbial communities, using the wealth of environmental rRNA sequences, and allows quantitative insight into the factors that underlie the distribution of lineages among environments.
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              Ultra-high-throughput microbial community analysis on the Illumina HiSeq and MiSeq platforms

              DNA sequencing continues to decrease in cost with the Illumina HiSeq2000 generating up to 600 Gb of paired-end 100 base reads in a ten-day run. Here we present a protocol for community amplicon sequencing on the HiSeq2000 and MiSeq Illumina platforms, and apply that protocol to sequence 24 microbial communities from host-associated and free-living environments. A critical question as more sequencing platforms become available is whether biological conclusions derived on one platform are consistent with what would be derived on a different platform. We show that the protocol developed for these instruments successfully recaptures known biological results, and additionally that biological conclusions are consistent across sequencing platforms (the HiSeq2000 versus the MiSeq) and across the sequenced regions of amplicons.
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                Author and article information

                Contributors
                simonlax@uchicago.edu
                jhampton-marcell@anl.gov
                sgibbons@uchicago.edu
                gbcolares@gmail.com
                Dansmith01@gmail.com
                jonathan.eisen@gmail.com
                gilbertjack@anl.gov
                Journal
                Microbiome
                Microbiome
                Microbiome
                BioMed Central (London )
                2049-2618
                12 May 2015
                12 May 2015
                2015
                : 3
                Affiliations
                [ ]Institute for Genomics and Systems Biology, Biosciences Department, Argonne National Laboratory, 9700 South Cass Avenue, Argonne, IL 60439 USA
                [ ]Department of Ecology and Evolution, University of Chicago, 1101 E 57th Street, Chicago, IL 60637 USA
                [ ]Graduate Program in Biophysical Sciences, University of Chicago, 5801 South Ellis Avenue, Chicago, USA
                [ ]Departamento de Biologia, Universidade Federal do Ceará, Avenida da Universidade, 2853 - Benfica, Fortaleza, CE 60440-900 Brazil
                [ ]Alkek Center for Metagenomics and Microbiome Research, Department of Molecular Virology and Microbiology, Baylor College of Medicine, 1 Baylor Plaza, Houston, TX 77030 USA
                [ ]Department of Evolution and Ecology, University of California, 1544 Newton Ct, Davis, CA USA
                [ ]Department of Medical Microbiology and Immunology, University of California, 1544 Newton Ct, Davis, CA USA
                [ ]UC Davis Genome Center, University of California, 1 Shields Avenue, Davis, CA USA
                [ ]Marine Biological Laboratory, 7 MBL Street, Woods Hole, MA 02543 USA
                [ ]College of Environmental and Resource Sciences, Zhejiang University, 38 Zheda Road, Hangzhou, 310058 China
                Article
                82
                10.1186/s40168-015-0082-9
                4427962
                25969737
                © Lax et al.; licensee BioMed Central. 2015

                This is an Open Access article distributed under the terms of the Creative Commons Attribution License ( http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver ( http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.

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