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mSystems. 2016 Apr 19;1(2). pii: e00022-16. eCollection 2016 Mar-Apr.

Geography and Location Are the Primary Drivers of Office Microbiome Composition.

Author information

1
Department of Biological Sciences, Northern Arizona University, Flagstaff, Arizona, USA; Center for Microbial Genetics and Genomics, Northern Arizona University, Flagstaff, Arizona, USA.
2
Department of Biology, San Diego State University, San Diego, California, USA.
3
Department of Civil Engineering, University of Toronto, Toronto, Ontario, Canada.
4
Department of Mathematics and Statistics, Northern Arizona University, Flagstaff, Arizona, USA.
5
Department of Computer of Science, University of California San Diego, San Diego, California, USA; Department of Pediatrics, University of California San Diego, San Diego, California, USA.
6
Department of Civil Engineering, University of Toronto, Toronto, Ontario, Canada; Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada.

Abstract

In the United States, humans spend the majority of their time indoors, where they are exposed to the microbiome of the built environment (BE) they inhabit. Despite the ubiquity of microbes in BEs and their potential impacts on health and building materials, basic questions about the microbiology of these environments remain unanswered. We present a study on the impacts of geography, material type, human interaction, location in a room, seasonal variation, and indoor and microenvironmental parameters on bacterial communities in offices. Our data elucidate several important features of microbial communities in BEs. First, under normal office environmental conditions, bacterial communities do not differ on the basis of surface material (e.g., ceiling tile or carpet) but do differ on the basis of the location in a room (e.g., ceiling or floor), two features that are often conflated but that we are able to separate here. We suspect that previous work showing differences in bacterial composition with surface material was likely detecting differences based on different usage patterns. Next, we find that offices have city-specific bacterial communities, such that we can accurately predict which city an office microbiome sample is derived from, but office-specific bacterial communities are less apparent. This differs from previous work, which has suggested office-specific compositions of bacterial communities. We again suspect that the difference from prior work arises from different usage patterns. As has been previously shown, we observe that human skin contributes heavily to the composition of BE surfaces. IMPORTANCE Our study highlights several points that should impact the design of future studies of the microbiology of BEs. First, projects tracking changes in BE bacterial communities should focus sampling efforts on surveying different locations in offices and in different cities but not necessarily different materials or different offices in the same city. Next, disturbance due to repeated sampling, though detectable, is small compared to that due to other variables, opening up a range of longitudinal study designs in the BE. Next, studies requiring more samples than can be sequenced on a single sequencing run (which is increasingly common) must control for run effects by including some of the same samples in all of the sequencing runs as technical replicates. Finally, detailed tracking of indoor and material environment covariates is likely not essential for BE microbiome studies, as the normal range of indoor environmental conditions is likely not large enough to impact bacterial communities.

KEYWORDS:

bacteria; built environment; fungi; microbiome

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