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Research Article | Applied and Environmental Science

Microbial Community Patterns Associated with Automated Teller Machine Keypads in New York City

Holly M. Bik, Julia M. Maritz, Albert Luong, Hakdong Shin, Maria Gloria Dominguez-Bello, Jane M. Carlton
Timothy E. Mattes, Editor
Holly M. Bik
aCenter for Genomics and Systems Biology, Department of Biology, New York University, New York, New York, USA
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  • ORCID record for Holly M. Bik
Julia M. Maritz
aCenter for Genomics and Systems Biology, Department of Biology, New York University, New York, New York, USA
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Albert Luong
bHuman Microbiome Program, New York University School of Medicine, New York, New York, USA
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Hakdong Shin
bHuman Microbiome Program, New York University School of Medicine, New York, New York, USA
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Maria Gloria Dominguez-Bello
bHuman Microbiome Program, New York University School of Medicine, New York, New York, USA
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Jane M. Carlton
aCenter for Genomics and Systems Biology, Department of Biology, New York University, New York, New York, USA
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Timothy E. Mattes
University of Iowa
Roles: Editor
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DOI: 10.1128/mSphere.00226-16
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Figures

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  • Supplemental Material
  • FIG 1
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    FIG 1

    Map and population demographic metadata of sample sites in New York City. Microbial swab samples were collected at automated teller machines (ATMs) in eight neighborhood tabulation areas (NTAs), representing three boroughs of New York City (Manhattan, Queens, and Brooklyn). NTA population demographics, representing 5-year estimates from the United States Census Bureau’s American Community Survey (ACS) (2008 to 2012), were obtained from the NYC open data portal (https://nycopendata.socrata.com/ ). “ancestry” demographics represent write-in responses from a small subset of survey respondents, enabling respondents to report ethnic origins that are not otherwise captured in questions pertaining to race or foreign-born status in the ACS. Age data represent years. (Map data © 2016 Google.)

  • FIG 2
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    FIG 2

    Relative abundances of bacterial/archaeal groups in 16S rRNA data set. (A) Microbial taxonomy summarized at phylum level. (B) Microbial taxonomy summarized at the class level; the legend displays only the top 15 most abundant taxa in the bar chart. Plots were generated in QIIME using abundance-filtered OTU tables with control OTUs subtracted. MH, Marble Hill; S., South; W., West.

  • FIG 3
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    FIG 3

    Relative abundances of eukaryotic groups in 18S rRNA data set. Summary of level 3 taxonomy data from the SILVA database, showing higher-level eukaryotic ranks observed in the ATM keypad data set. The plot was generated in QIIME using abundance-filtered OTU tables with control OTUs subtracted.

  • FIG 4
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    FIG 4

    Beta-diversity analyses of microbial taxa recovered from ATM keypads. Data represent results of unweighted Unifrac PCoAs for 16S rRNA for bacteria/archaea (A to C) and 18S rRNA for eukaryotes (D to F), showing no obvious clustering of microbial assemblages according to NYC neighborhood (A and D), census population demographics (race group with highest proportion in each neighborhood) (B and E), or type of site where ATM was located (F). The strongest clustering pattern in the data set was a technical artifact observed for 16S rRNA samples sequenced across two Illumina MiSeq runs (C).

  • FIG 5
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    FIG 5

    SourceTracker analysis of bacterial/archaeal assemblages on ATM keypads. Closed-reference OTUs (16S rRNA only) from this study were compared to 12 published datasets representing a range of potential source habitats (human body, building surfaces, indoor/outdoor air). The majority of microbes on each ATM keypad were derived from an unknown source. The most common identified source across all ATMs appeared to be household surfaces (rest room, kitchen, pillows, and televisions) and outdoor air. Gold stars denote the four ATMs in this study located at outdoor sites.

  • FIG 6
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    FIG 6

    Linear discriminant analysis (LDA) effect size (LEfSe) analysis to determine microbial biomarker taxa across sample groups. (A) Eukaryotic 18S rRNA OTUs significantly enriched across census population demographics (predominant race group in each NTA). (B) Bacterial/archaeal genera significantly enriched across different ATM site types in 16S rRNA data set.

Tables

  • Figures
  • Supplemental Material
  • TABLE 1

    PERMANOVA test for statistical significance of sample groupingsa

    Category16S rRNA
    (Bacteria/archaea)
    18S rRNA
    (eukaryotes)
    Pseudo-F valueP valuePseudo-F valueP value
    Borough1.2744 0.0279 1.3959 0.0028
    Neighborhood1.3184 0.0003 1.1880 0.0043
    Site type
    (Bank, store, etc.)
    1.04970.25931.1942 0.0257
    ATM location (indoor/outdoor)1.7337 0.0048 NANA
    Population demographics (race)1.3559 0.0038 1.11410.1040
    Illumina run3.2439 0.0001 NANA
    • ↵a Statistical tests were performed on unweighted Unifrac distance matrices (where PCoAs were generated from abundance-filtered OTU tables with control OTUs subtracted), using 10,000 permutations per test. Bold numbers represent significant P values of <0.05. Pseudo-F numbers represent F values by permutation.

  • TABLE 2

    Number of significantly discriminative taxa reported in LefSe analysis (absolute LDA score, >2.0)a

    CategoryNo. of significantly discriminative taxa
    16S rRNA
    (Bacteria/archaea)
    18S rRNA
    (eukaryotes)
    L5 taxa
    (family)
    L6 taxa
    (genus)
    OTUsL5 taxa
    (family)
    L6 taxa
    (genus)
    OTUs
    Borough/neighborhood00071136
    Site type
    (Bank, store, etc.)
    2933525
    ATM location (indoor/outdoor)93235148NANANA
    Population demographics (race)1213616
    • ↵a LefSe analyses were performed on normalized BIOM tables from open reference OTU picking, following abundance-based filtering and removal of OTUs present in kit control samples. LefSe analyses were performed on OTU tables summarized at the L5 (family) and L6 (genus) taxonomy levels, as well as on unsummarized OTU tables. NA, ATM location comparisons were not possible for 18S rRNA, as only indoor ATMs were included in the eukaryotic data set.

Supplemental Material

  • Figures
  • Tables
  • Figure S1

    Unweighted Unifrac PCoAs showing distinct clustering of control samples. PCoAs were performed using abundance-filtered OTU tables, after removal of chimeras and OTUs that failed to align to reference rRNA databases. (A) 16S rRNA data for bacterial/archaeal taxa rarefied at 2,200 sequences per sample. (B) 18S rRNA data for eukaryotic taxa rarefied at 25,000 sequences per sample. Download Figure S1, PDF file, 0.1 MB.

    Copyright © 2016 Bik et al.

    This content is distributed under the terms of the Creative Commons Attribution 4.0 International license .

  • Figure S2

    SourceTracker results for 16S rRNA data before subtracting OTUs from control samples. SourceTracker analyses were performed on closed-reference OTUs from this study and 12 public “source” datasets obtained from a previous meta-analysis (68). All “kit control” data represent sequence reads generated from control swab samples in the present study. Download Figure S2, PDF file, 0.2 MB.

    Copyright © 2016 Bik et al.

    This content is distributed under the terms of the Creative Commons Attribution 4.0 International license .

  • Figure S3

    SourceTracker results for 18S rRNA data before subtracting OTUs from control samples. SourceTracker analyses were performed on open-reference OTUs obtained from the present study only (with control samples marked as a potential “source”). All “kit control” data represent sequence reads generated from control swab samples in the present study. Download Figure S3, PDF file, 0.1 MB.

    Copyright © 2016 Bik et al.

    This content is distributed under the terms of the Creative Commons Attribution 4.0 International license .

  • Figure S4

    Alpha diversity by neighborhood calculated using Faith’s phylogenetic diversity metric. Rarefaction curves were calculated in QIIME using abundance-filtered OTU tables with control OTUs subtracted. (A) 16S rRNA data for bacterial/archaeal taxa rarefied at 1,700 sequences per sample. (B) 18S rRNA data for eukaryotic taxa rarefied at 8,900 sequences per sample. Download Figure S4, PDF file, 0.4 MB.

    Copyright © 2016 Bik et al.

    This content is distributed under the terms of the Creative Commons Attribution 4.0 International license .

  • Figure S5

    Linear discriminant analysis (LDA) effect size (LEfSe) analysis showing prokaryotic biomarker taxa across indoor/outdoor ATM keypads. Analysis was carried out on a 16S rRNA data set (normalized per sample) subjected to abundance-based filtering and subtraction of control sample OTUs. Download Figure S5, PDF file, 1.7 MB.

    Copyright © 2016 Bik et al.

    This content is distributed under the terms of the Creative Commons Attribution 4.0 International license .

  • Table S1

    Summary of sequence reads and OTU counts for all ATM swab samples. Summarized data reflect the outputs of open reference OTU picking in QIIME 1.8 for 16S rRNA (bacterial/archaeal) and 18S rRNA (eukaryotic) amplicons. Summaries are also reported for sequential downstream filtering steps to remove alignment failures and chimeras, followed by abundance filtering and removal of all OTUs present in blank control samples. Download Table S1, PDF file, 0.1 MB.

    Copyright © 2016 Bik et al.

    This content is distributed under the terms of the Creative Commons Attribution 4.0 International license .

  • Figure S6

    Sequence reads and OTU counts for 16S rRNA data. Reads and OTUs were calculated following open reference OTU clustering in the QIIME pipeline, carried out using uclust with a 97% similarity cutoff. OTU counts exclude clusters containing <2 reads. Yellow circles indicate blank control samples. Data shown represents the raw results from open reference OTU picking (see the first four columns of Table S1), without application of any downstream data filtering based on alignment failures, chimeras, or OTU abundances. Download Figure S6, PDF file, 0.2 MB.

    Copyright © 2016 Bik et al.

    This content is distributed under the terms of the Creative Commons Attribution 4.0 International license .

  • Figure S7

    Sequence reads and OTU counts for 18S rRNA data. Reads and OTUs were calculated following open reference OTU clustering in the QIIME pipeline, carried out using uclust with a 99% similarity cutoff. OTU counts exclude clusters containing <2 reads. Yellow circles indicate blank control samples. Data shown represent the raw results from open reference OTU picking (see the first four columns of Table S1), without application of any downstream data filtering based on alignment failures, chimeras, or OTU abundances. Download Figure S7, PDF file, 0.2 MB.

    Copyright © 2016 Bik et al.

    This content is distributed under the terms of the Creative Commons Attribution 4.0 International license .

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Microbial Community Patterns Associated with Automated Teller Machine Keypads in New York City
Holly M. Bik, Julia M. Maritz, Albert Luong, Hakdong Shin, Maria Gloria Dominguez-Bello, Jane M. Carlton
mSphere Nov 2016, 1 (6) e00226-16; DOI: 10.1128/mSphere.00226-16

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Microbial Community Patterns Associated with Automated Teller Machine Keypads in New York City
Holly M. Bik, Julia M. Maritz, Albert Luong, Hakdong Shin, Maria Gloria Dominguez-Bello, Jane M. Carlton
mSphere Nov 2016, 1 (6) e00226-16; DOI: 10.1128/mSphere.00226-16
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    • ABSTRACT
    • INTRODUCTION
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KEYWORDS

16S rRNA
18S rRNA
New York City
automated teller machine
environmental sequencing
urban microbiome

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