Architectural Design Drives the Biogeography of Indoor Bacterial Communities
Publication Date
January 29, 2014
Journal
PLOS ONE
Authors
Steven W. Kembel, James F. Meadow, Timothy K. O’connor, Gwynne Mhuireach, et al
Volume
9
Issue
1
Pages
e87093
DOI
https://dx.plos.org/10.1371/journal.pone.0087093
Publisher URL
http://journals.plos.org/plosone/article?id=10.1371%2Fjournal.pone.0087093
PubMed
http://www.ncbi.nlm.nih.gov/pubmed/24489843
PubMed Central
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3906134
Europe PMC
http://europepmc.org/abstract/MED/24489843
Web of Science
000330570000100
Scopus
84897846339
Mendeley
http://www.mendeley.com/research/architectural-design-drives-biogeography-indoor-bacterial-communities
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Mendeley | Further Information

{"title"=>"Architectural design drives the biogeography of indoor bacterial communities", "type"=>"journal", "authors"=>[{"first_name"=>"Steven W.", "last_name"=>"Kembel", "scopus_author_id"=>"8682558800"}, {"first_name"=>"James F.", "last_name"=>"Meadow", "scopus_author_id"=>"54785959100"}, {"first_name"=>"Timothy K.", "last_name"=>"O'Connor", "scopus_author_id"=>"55676534300"}, {"first_name"=>"Gwynne", "last_name"=>"Mhuireach", "scopus_author_id"=>"57191951976"}, {"first_name"=>"Dale", "last_name"=>"Northcutt", "scopus_author_id"=>"15751568000"}, {"first_name"=>"Jeff", "last_name"=>"Kline", "scopus_author_id"=>"9037066100"}, {"first_name"=>"Maxwell", "last_name"=>"Moriyama", "scopus_author_id"=>"55744429700"}, {"first_name"=>"G. Z.", "last_name"=>"Brown", "scopus_author_id"=>"12040073900"}, {"first_name"=>"Brendan J.M.", "last_name"=>"Bohannan", "scopus_author_id"=>"7004020596"}, {"first_name"=>"Jessica L.", "last_name"=>"Green", "scopus_author_id"=>"55463724300"}], "year"=>2014, "source"=>"PLoS ONE", "identifiers"=>{"pmid"=>"24489843", "doi"=>"10.1371/journal.pone.0087093", "sgr"=>"84897846339", "isbn"=>"10.1371/journal.pone.0087093", "scopus"=>"2-s2.0-84897846339", "issn"=>"19326203", "pui"=>"373059913"}, "id"=>"66b445dc-e431-3ba2-ab15-f82633e2613e", "abstract"=>"BACKGROUND: Architectural design has the potential to influence the microbiology of the built environment, with implications for human health and well-being, but the impact of design on the microbial biogeography of buildings remains poorly understood. In this study we combined microbiological data with information on the function, form, and organization of spaces from a classroom and office building to understand how design choices influence the biogeography of the built environment microbiome.\\n\\nRESULTS: Sequencing of the bacterial 16S gene from dust samples revealed that indoor bacterial communities were extremely diverse, containing more than 32,750 OTUs (operational taxonomic units, 97% sequence similarity cutoff), but most communities were dominated by Proteobacteria, Firmicutes, and Deinococci. Architectural design characteristics related to space type, building arrangement, human use and movement, and ventilation source had a large influence on the structure of bacterial communities. Restrooms contained bacterial communities that were highly distinct from all other rooms, and spaces with high human occupant diversity and a high degree of connectedness to other spaces via ventilation or human movement contained a distinct set of bacterial taxa when compared to spaces with low occupant diversity and low connectedness. Within offices, the source of ventilation air had the greatest effect on bacterial community structure.\\n\\nCONCLUSIONS: Our study indicates that humans have a guiding impact on the microbial biodiversity in buildings, both indirectly through the effects of architectural design on microbial community structure, and more directly through the effects of human occupancy and use patterns on the microbes found in different spaces and space types. The impact of design decisions in structuring the indoor microbiome offers the possibility to use ecological knowledge to shape our buildings in a way that will select for an indoor microbiome that promotes our health and well-being.", "link"=>"http://www.mendeley.com/research/architectural-design-drives-biogeography-indoor-bacterial-communities", "reader_count"=>153, "reader_count_by_academic_status"=>{"Unspecified"=>1, "Professor > Associate Professor"=>6, "Researcher"=>43, "Student > Doctoral Student"=>13, "Student > Ph. D. Student"=>50, "Student > Postgraduate"=>3, "Student > Master"=>12, "Other"=>4, "Student > Bachelor"=>12, "Professor"=>9}, "reader_count_by_user_role"=>{"Unspecified"=>1, "Professor > Associate Professor"=>6, "Researcher"=>43, "Student > Doctoral Student"=>13, "Student > Ph. D. Student"=>50, "Student > Postgraduate"=>3, "Student > Master"=>12, "Other"=>4, "Student > Bachelor"=>12, "Professor"=>9}, "reader_count_by_subject_area"=>{"Unspecified"=>5, "Agricultural and Biological Sciences"=>72, "Arts and Humanities"=>3, "Chemistry"=>1, "Computer Science"=>4, "Earth and Planetary Sciences"=>1, "Engineering"=>14, "Environmental Science"=>10, "Biochemistry, Genetics and Molecular Biology"=>13, "Nursing and Health Professions"=>1, "Mathematics"=>1, "Medicine and Dentistry"=>9, "Design"=>6, "Psychology"=>1, "Social Sciences"=>6, "Immunology and Microbiology"=>6}, "reader_count_by_subdiscipline"=>{"Medicine and Dentistry"=>{"Medicine and Dentistry"=>9}, "Social Sciences"=>{"Social Sciences"=>6}, "Psychology"=>{"Psychology"=>1}, "Mathematics"=>{"Mathematics"=>1}, "Unspecified"=>{"Unspecified"=>5}, "Environmental Science"=>{"Environmental Science"=>10}, "Arts and Humanities"=>{"Arts and Humanities"=>3}, "Design"=>{"Design"=>6}, "Engineering"=>{"Engineering"=>14}, "Chemistry"=>{"Chemistry"=>1}, "Earth and Planetary Sciences"=>{"Earth and Planetary Sciences"=>1}, "Immunology and Microbiology"=>{"Immunology and Microbiology"=>6}, "Agricultural and Biological Sciences"=>{"Agricultural and Biological Sciences"=>72}, "Computer Science"=>{"Computer Science"=>4}, "Nursing and Health Professions"=>{"Nursing and Health Professions"=>1}, "Biochemistry, Genetics and Molecular Biology"=>{"Biochemistry, Genetics and Molecular Biology"=>13}}, "reader_count_by_country"=>{"Argentina"=>1, "Belgium"=>1, "United States"=>12, "Luxembourg"=>1, "Brazil"=>1, "United Kingdom"=>2, "South Africa"=>1, "Germany"=>1, "India"=>1}, "group_count"=>12}

CrossRef

Scopus | Further Information

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  • {"files"=>["https://ndownloader.figshare.com/files/1365819"], "description"=>"<p><b>a)</b> The first axis is constrained by whether or not offices have operable window louvers (blue) or not (red). Taxon names on either side are grouped from the 25 strongest weighting OTUs in either direction. <b>b)</b><i>Deinococcus</i> were 1.7 times more abundant in mechanically ventilated offices compared to window ventilated offices. <b>c)</b> The opposite pattern was observed for <i>Methylobacterium</i> OTUs, which were 1.8 times more abundant in window ventilated offices. Boxplots delineate (from bottom) minimum, Q1, median, Q3, and maximum values; notches indicate 95% confidence intervals. <b>d)</b> Cross-sectional view of representative Lillis Hall offices. Offices on the south side of the building (left) received primarily mechanically ventilated air, while offices on the north side of the building (right) are equipped with operable windows as a primary ventilation air source.</p>", "links"=>[], "tags"=>["Computational biology", "Sequence analysis", "ecology", "biodiversity", "biogeography", "Community Ecology", "Ecological environments", "microbial ecology", "microbiology", "bacteriology", "Architectural engineering", "microbial", "communities", "ventilation"], "article_id"=>917563, "categories"=>["Biological Sciences", "Engineering"], "users"=>["Steven W. Kembel", "James F. Meadow", "Timothy K. O’Connor", "Gwynne Mhuireach", "Dale Northcutt", "Jeff Kline", "Maxwell Moriyama", "G. Z. Brown", "Brendan J. M. Bohannan", "Jessica L. Green"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0087093.g005", "stats"=>{"downloads"=>1, "page_views"=>8, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Offices_contain_significantly_different_dust_microbial_communities_depending_on_ventilation_source_/917563", "title"=>"Offices contain significantly different dust microbial communities depending on ventilation source.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-01-29 03:13:52"}
  • {"files"=>["https://ndownloader.figshare.com/files/1365800"], "description"=>"<p>Examples in the left column follow classic network representation, while those in the right column embody the architectural translation of networks. Shaded nodes and building spaces correspond to centrality measures <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0087093#pone.0087093-Freeman1\" target=\"_blank\">[22]</a> of <i>betweenness</i> (the number of shortest paths between all pairs of spaces that pass through a given space over the sum of all shortest paths between all pairs of spaces in the building) and <i>degree</i> (the number of connections a space has to other spaces); <i>connectance distance</i> (the number of doors between any two spaces) is a pairwise metric, shown here as the range of connectance distance values for each complete network/building. Since <i>betweenness</i> and <i>degree</i> strongly co-vary and are both measures of network centrality <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0087093#pone.0087093-Freeman1\" target=\"_blank\">[22]</a>, they are considered together in some analyses.</p>", "links"=>[], "tags"=>["Computational biology", "Sequence analysis", "ecology", "biodiversity", "biogeography", "Community Ecology", "Ecological environments", "microbial ecology", "microbiology", "bacteriology", "Architectural engineering", "metrics", "quantify", "spatial", "spaces", "lillis"], "article_id"=>917544, "categories"=>["Biological Sciences", "Engineering"], "users"=>["Steven W. Kembel", "James F. Meadow", "Timothy K. O’Connor", "Gwynne Mhuireach", "Dale Northcutt", "Jeff Kline", "Maxwell Moriyama", "G. Z. Brown", "Brendan J. M. Bohannan", "Jessica L. Green"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0087093.g002", "stats"=>{"downloads"=>1, "page_views"=>9, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Network_analysis_metrics_used_to_quantify_spatial_arrangement_of_spaces_within_Lillis_Hall_/917544", "title"=>"Network analysis metrics used to quantify spatial arrangement of spaces within Lillis Hall.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-01-29 03:13:52"}
  • {"files"=>["https://ndownloader.figshare.com/files/1365798"], "description"=>"<p>Restrooms (brown), offices (blue) and classrooms (yellow) are shown to illustrate space type distribution throughout Lillis. The first two floors of the building are primarily devoted to classrooms and share a similar floor-plan. The 3rd and 4th floors contain most offices in the building and also share a similar floor-plan. The building has a basement and penthouse spaces; these are largely building support spaces, including mechanical rooms and storage.</p>", "links"=>[], "tags"=>["Computational biology", "Sequence analysis", "ecology", "biodiversity", "biogeography", "Community Ecology", "Ecological environments", "microbial ecology", "microbiology", "bacteriology", "Architectural engineering", "layout", "floors", "lillis"], "article_id"=>917542, "categories"=>["Biological Sciences", "Engineering"], "users"=>["Steven W. Kembel", "James F. Meadow", "Timothy K. O’Connor", "Gwynne Mhuireach", "Dale Northcutt", "Jeff Kline", "Maxwell Moriyama", "G. Z. Brown", "Brendan J. M. Bohannan", "Jessica L. Green"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0087093.g001", "stats"=>{"downloads"=>2, "page_views"=>9, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Architectural_layout_for_two_of_four_floors_in_Lillis_Hall_/917542", "title"=>"Architectural layout for two of four floors in Lillis Hall.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-01-29 03:13:52"}
  • {"files"=>["https://ndownloader.figshare.com/files/1365808"], "description"=>"<p>Samples are organized by space type, and relative abundances are shown for groups comprising more than 1% (for phylum and class level) and 4% (for order level).</p>", "links"=>[], "tags"=>["Computational biology", "Sequence analysis", "ecology", "biodiversity", "biogeography", "Community Ecology", "Ecological environments", "microbial ecology", "microbiology", "bacteriology", "Architectural engineering", "taxonomic", "bacterial", "communities", "sampled", "lillis"], "article_id"=>917552, "categories"=>["Biological Sciences", "Engineering"], "users"=>["Steven W. Kembel", "James F. Meadow", "Timothy K. O’Connor", "Gwynne Mhuireach", "Dale Northcutt", "Jeff Kline", "Maxwell Moriyama", "G. Z. Brown", "Brendan J. M. Bohannan", "Jessica L. Green"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0087093.g003", "stats"=>{"downloads"=>1, "page_views"=>13, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_The_taxonomic_composition_of_bacterial_communities_sampled_from_dust_in_Lillis_Hall_/917552", "title"=>"The taxonomic composition of bacterial communities sampled from dust in Lillis Hall.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-01-29 03:13:52"}
  • {"files"=>["https://ndownloader.figshare.com/files/1365821"], "description"=>"<p>Variance explained (<i>R<sup>2</sup></i>) and statistical significance (<i>P-value</i>) quantified with a PERMANOVA test; since <i>P-values</i> are from permutational tests involving 999 permutations, they are only reported down to 0.001. All variables and their respective units are described in the methods section and <a href=\"http://www.plosone.org/article/info:doi/10.1371/journal.pone.0087093#pone.0087093.s003\" target=\"_blank\">Table S1</a>.</p>*<p>detrended using daily averages.</p>", "links"=>[], "tags"=>["Computational biology", "Sequence analysis", "ecology", "biodiversity", "biogeography", "Community Ecology", "Ecological environments", "microbial ecology", "microbiology", "bacteriology", "Architectural engineering", "dissimilarity", "bacterial", "communities", "explained", "variables", "lillis"], "article_id"=>917565, "categories"=>["Biological Sciences", "Engineering"], "users"=>["Steven W. Kembel", "James F. Meadow", "Timothy K. O’Connor", "Gwynne Mhuireach", "Dale Northcutt", "Jeff Kline", "Maxwell Moriyama", "G. Z. Brown", "Brendan J. M. Bohannan", "Jessica L. Green"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0087093.t001", "stats"=>{"downloads"=>0, "page_views"=>15, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Variance_in_biological_dissimilarity_among_bacterial_communities_from_all_spaces_as_well_as_just_offices_Canberra_distance_explained_by_different_variables_in_Lillis_Hall_/917565", "title"=>"Variance in biological dissimilarity among bacterial communities from all spaces, as well as just offices, (Canberra distance) explained by different variables in Lillis Hall.", "pos_in_sequence"=>0, "defined_type"=>3, "published_date"=>"2014-01-29 03:13:52"}
  • {"files"=>["https://ndownloader.figshare.com/files/1365820"], "description"=>"<p>When only considering a single space type, biological similarity (y-axis; 1 - Canberra distance) decreases with connectance distance (number of intermediate space boundaries [e.g., doors] one would walk through to travel the shortest distance between any two spaces) (Mantel test; <i>R</i> = 0.189; <i>P</i> = 0.002). The same pattern was also observed at the whole-building scale (not shown; Mantel test; <i>R</i> = 0.112; <i>P</i> = 0.001).</p>", "links"=>[], "tags"=>["Computational biology", "Sequence analysis", "ecology", "biodiversity", "biogeography", "Community Ecology", "Ecological environments", "microbial ecology", "microbiology", "bacteriology", "Architectural engineering", "lillis", "distance-decay"], "article_id"=>917564, "categories"=>["Biological Sciences", "Engineering"], "users"=>["Steven W. Kembel", "James F. Meadow", "Timothy K. O’Connor", "Gwynne Mhuireach", "Dale Northcutt", "Jeff Kline", "Maxwell Moriyama", "G. Z. Brown", "Brendan J. M. Bohannan", "Jessica L. Green"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0087093.g006", "stats"=>{"downloads"=>2, "page_views"=>15, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Offices_in_Lillis_Hall_show_a_strong_distance_decay_pattern_/917564", "title"=>"Offices in Lillis Hall show a strong distance-decay pattern.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-01-29 03:13:52"}
  • {"files"=>["https://ndownloader.figshare.com/files/1365811"], "description"=>"<p>Points represent centroids (±SE) from distance based redundancy analysis (DB-RDA). Space types hold significantly different communities (<i>P</i> = 0.005), though this is driven primarily by restrooms. Bacterial OTUs that have the strongest influence in sample dissimilarities are shown at the margins; numbers in parentheses indicate multiple OTUs in the same genus. Centrality (along y-axis) represents network betweenness and degree; human occupancy (along x-axis) represents annual occupied hours and human diversity. All four correlates (simple linear models as a factor of ordination axis) are significant along their respective axes (all <i>P</i><0.001).</p>", "links"=>[], "tags"=>["Computational biology", "Sequence analysis", "ecology", "biodiversity", "biogeography", "Community Ecology", "Ecological environments", "microbial ecology", "microbiology", "bacteriology", "Architectural engineering", "communities", "correlated", "centrality"], "article_id"=>917555, "categories"=>["Biological Sciences", "Engineering"], "users"=>["Steven W. Kembel", "James F. Meadow", "Timothy K. O’Connor", "Gwynne Mhuireach", "Dale Northcutt", "Jeff Kline", "Maxwell Moriyama", "G. Z. Brown", "Brendan J. M. Bohannan", "Jessica L. Green"], "doi"=>"https://dx.doi.org/10.1371/journal.pone.0087093.g004", "stats"=>{"downloads"=>0, "page_views"=>4, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Dust_communities_within_a_building_cluster_by_space_type_and_are_strongly_correlated_with_building_centrality_and_human_occupancy_/917555", "title"=>"Dust communities within a building cluster by space type and are strongly correlated with building centrality and human occupancy.", "pos_in_sequence"=>0, "defined_type"=>1, "published_date"=>"2014-01-29 03:13:52"}
  • {"files"=>["https://ndownloader.figshare.com/files/1365823", "https://ndownloader.figshare.com/files/1365824", "https://ndownloader.figshare.com/files/1365825"], "description"=>"<div><p>Background</p><p>Architectural design has the potential to influence the microbiology of the built environment, with implications for human health and well-being, but the impact of design on the microbial biogeography of buildings remains poorly understood. In this study we combined microbiological data with information on the function, form, and organization of spaces from a classroom and office building to understand how design choices influence the biogeography of the built environment microbiome.</p><p>Results</p><p>Sequencing of the bacterial 16S gene from dust samples revealed that indoor bacterial communities were extremely diverse, containing more than 32,750 OTUs (operational taxonomic units, 97% sequence similarity cutoff), but most communities were dominated by Proteobacteria, Firmicutes, and Deinococci. Architectural design characteristics related to space type, building arrangement, human use and movement, and ventilation source had a large influence on the structure of bacterial communities. Restrooms contained bacterial communities that were highly distinct from all other rooms, and spaces with high human occupant diversity and a high degree of connectedness to other spaces via ventilation or human movement contained a distinct set of bacterial taxa when compared to spaces with low occupant diversity and low connectedness. Within offices, the source of ventilation air had the greatest effect on bacterial community structure.</p><p>Conclusions</p><p>Our study indicates that humans have a guiding impact on the microbial biodiversity in buildings, both indirectly through the effects of architectural design on microbial community structure, and more directly through the effects of human occupancy and use patterns on the microbes found in different spaces and space types. The impact of design decisions in structuring the indoor microbiome offers the possibility to use ecological knowledge to shape our buildings in a way that will select for an indoor microbiome that promotes our health and well-being.</p></div>", "links"=>[], "tags"=>["Computational biology", "Sequence analysis", "ecology", "biodiversity", "biogeography", "Community Ecology", "Ecological environments", "microbial ecology", "microbiology", "bacteriology", "Architectural engineering", "drives", "indoor", "bacterial"], "article_id"=>917567, "categories"=>["Biological Sciences", "Engineering"], "users"=>["Steven W. Kembel", "James F. Meadow", "Timothy K. O’Connor", "Gwynne Mhuireach", "Dale Northcutt", "Jeff Kline", "Maxwell Moriyama", "G. Z. Brown", "Brendan J. M. Bohannan", "Jessica L. Green"], "doi"=>["https://dx.doi.org/10.1371/journal.pone.0087093.s001", "https://dx.doi.org/10.1371/journal.pone.0087093.s002", "https://dx.doi.org/10.1371/journal.pone.0087093.s003"], "stats"=>{"downloads"=>14, "page_views"=>19, "likes"=>0}, "figshare_url"=>"https://figshare.com/articles/_Architectural_Design_Drives_the_Biogeography_of_Indoor_Bacterial_Communities_/917567", "title"=>"Architectural Design Drives the Biogeography of Indoor Bacterial Communities", "pos_in_sequence"=>0, "defined_type"=>4, "published_date"=>"2014-01-29 03:13:52"}

PMC Usage Stats | Further Information

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