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

Abstract

In densely populated urban environments, the distribution of microbes and the drivers of microbial community assemblages are not well understood. In sprawling metropolitan habitats, the "urban microbiome" may represent a mix of human-associated and environmental taxa. Here we carried out a baseline study of automated teller machine (ATM) keypads in New York City (NYC). Our goal was to describe the biodiversity and biogeography of both prokaryotic and eukaryotic microbes in an urban setting while assessing the potential source of microbial assemblages on ATM keypads. Microbial swab samples were collected from three boroughs (Manhattan, Queens, and Brooklyn) during June and July 2014, followed by generation of Illumina MiSeq datasets for bacterial (16S rRNA) and eukaryotic (18S rRNA) marker genes. Downstream analysis was carried out in the QIIME pipeline, in conjunction with neighborhood metadata (ethnicity, population, age groups) from the NYC Open Data portal. Neither the 16S nor 18S rRNA datasets showed any clustering patterns related to geography or neighborhood demographics. Bacterial assemblages on ATM keypads were dominated by taxonomic groups known to be associated with human skin communities (Actinobacteria, Bacteroides, Firmicutes, and Proteobacteria), although SourceTracker analysis was unable to identify the source habitat for the majority of taxa. Eukaryotic assemblages were dominated by fungal taxa as well as by a low-diversity protist community containing both free-living and potentially pathogenic taxa (Toxoplasma, Trichomonas). Our results suggest that ATM keypads amalgamate microbial assemblages from different sources, including the human microbiome, eukaryotic food species, and potentially novel extremophilic taxa adapted to air or surfaces in the built environment. DNA obtained from ATM keypads may thus provide a record of both human behavior and environmental sources of microbes. IMPORTANCE Automated teller machine (ATM) keypads represent a specific and unexplored microhabitat for microbial communities. Although the number of built environment and urban microbial ecology studies has expanded greatly in recent years, the majority of research to date has focused on mass transit systems, city soils, and plumbing and ventilation systems in buildings. ATM surfaces, potentially retaining microbial signatures of human inhabitants, including both commensal taxa and pathogens, are interesting from both a biodiversity perspective and a public health perspective. By focusing on ATM keypads in different geographic areas of New York City with distinct population demographics, we aimed to characterize the diversity and distribution of both prokaryotic and eukaryotic microbes, thus making a unique contribution to the growing body of work focused on the "urban microbiome." In New York City, the surface area of urban surfaces in Manhattan far exceeds the geographic area of the island itself. We have only just begun to describe the vast array of microbial taxa that are likely to be present across diverse types of urban habitats.

Keywords: 16S rRNA; 18S rRNA; New York City; automated teller machine; environmental sequencing; urban microbiome.

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

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

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

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

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

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.