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Geography and Location Are the Primary Drivers of Office Microbiome Composition
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...
Autores principales: | , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
American Society for Microbiology
2016
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5069741/ https://www.ncbi.nlm.nih.gov/pubmed/27822521 http://dx.doi.org/10.1128/mSystems.00022-16 |
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author | Chase, John Fouquier, Jennifer Zare, Mahnaz Sonderegger, Derek L. Knight, Rob Kelley, Scott T. Siegel, Jeffrey Caporaso, J. Gregory |
author_facet | Chase, John Fouquier, Jennifer Zare, Mahnaz Sonderegger, Derek L. Knight, Rob Kelley, Scott T. Siegel, Jeffrey Caporaso, J. Gregory |
author_sort | Chase, John |
collection | PubMed |
description | 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. |
format | Online Article Text |
id | pubmed-5069741 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2016 |
publisher | American Society for Microbiology |
record_format | MEDLINE/PubMed |
spelling | pubmed-50697412016-11-07 Geography and Location Are the Primary Drivers of Office Microbiome Composition Chase, John Fouquier, Jennifer Zare, Mahnaz Sonderegger, Derek L. Knight, Rob Kelley, Scott T. Siegel, Jeffrey Caporaso, J. Gregory mSystems Research Article 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. American Society for Microbiology 2016-04-19 /pmc/articles/PMC5069741/ /pubmed/27822521 http://dx.doi.org/10.1128/mSystems.00022-16 Text en Copyright © 2016 Chase et al. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution 4.0 International license (http://creativecommons.org/licenses/by/4.0/) . |
spellingShingle | Research Article Chase, John Fouquier, Jennifer Zare, Mahnaz Sonderegger, Derek L. Knight, Rob Kelley, Scott T. Siegel, Jeffrey Caporaso, J. Gregory Geography and Location Are the Primary Drivers of Office Microbiome Composition |
title | Geography and Location Are the Primary Drivers of Office Microbiome Composition |
title_full | Geography and Location Are the Primary Drivers of Office Microbiome Composition |
title_fullStr | Geography and Location Are the Primary Drivers of Office Microbiome Composition |
title_full_unstemmed | Geography and Location Are the Primary Drivers of Office Microbiome Composition |
title_short | Geography and Location Are the Primary Drivers of Office Microbiome Composition |
title_sort | geography and location are the primary drivers of office microbiome composition |
topic | Research Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5069741/ https://www.ncbi.nlm.nih.gov/pubmed/27822521 http://dx.doi.org/10.1128/mSystems.00022-16 |
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