Geography and location are the primary drivers of office microbiome composition
- Published
- Accepted
- Subject Areas
- Bioinformatics, Ecology, Environmental Sciences, Microbiology
- Keywords
- built environment, microbiome
- Copyright
- © 2016 Chase et al.
- Licence
- This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, reproduction and adaptation in any medium and for any purpose provided that it is properly attributed. For attribution, the original author(s), title, publication source (PeerJ Preprints) and either DOI or URL of the article must be cited.
- Cite this article
- 2016. Geography and location are the primary drivers of office microbiome composition. PeerJ Preprints 4:e1797v1 https://doi.org/10.7287/peerj.preprints.1797v1
Abstract
North Americans 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 elucidates several important features of microbial communities in BEs. First, under normal office environmental conditions, bacterial communities do not differ based on surface material (e.g., ceiling tile or carpet), but do differ based on the location in a room (e.g., ceiling or floor), two features which are often conflated, but which we are able to separate here. We suspect that previous work showing differences in bacterial composition with surface material were 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.
Our study highlights several points that should impact the design of future studies of the microbiology of the BEs. First, projects tracking changes to BE bacterial communities should focus sampling effort 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 repeat sampling, though detectable, is small compared to other variables, opening up a range of longitudinal study designs in the built environment. 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 on all 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.
Author Comment
This article has been submitted for peer-review to mSystems.
Supplemental Information
Figure 1: Experimental design
(a) Configuration of sampling site in Flagstaff office 1. This configuration was similar to those set up in all offices. Signs on the wall adjacent to wall sampling plate describe the project, as request that the materials not be touched. (b) Diagram of single sampling plate illustrating nine sampling swatches (circles) of three different materials, one row for tracking equilibrium relative humidity of the materials (Row #1), one row for infrequent sampling (Row #2), and one row for frequent sampling (Row #3). (c) Samples were collected from rows 2 and 3 of all sampling plates from three offices in each of our three cities in four intensive sampling periods over the course of one year. Coloring of sampling swatches in this figure illustrates the change in bacterial Phylogenetic Diversity over the year.
Figure 2: Bacterial community dissimilarity
(a) Weighted UniFrac, a quantitative measure, and (b) unweighted UniFrac, a qualitative measure for the Flagstaff samples (similar patterns are observed across all cities). Darker colors indicate that groups of samples are more dissimilar and lighter colors indicate that groups of samples are more similar. The labels indicate the location in the office followed by the material type. For example, Ceiling/Carpet indicates the samples of carpet that are installed on the ceiling sampling plate, and investigation of the first column of (a) shows that the carpet samples on the ceiling are more similar to the carpet samples on the wall than they are to the carpet samples on the floor.
Figure 3: Disturbance due to repeat sampling, though detectable, is small compared to other variables
(a) Phylogenetic diversity (PD) of frequently sampled swatches (“Row 2”, see Figure 1b) was consistently lower than PD of the frequently sampled swatches, suggesting that sampling decreases the PD of the sites. (b) Weighted UniFrac shows that the samples with the same sampling frequency are more similar to each other than samples with different sampling frequencies, suggesting an effect of repeat sampling. However comparison of this difference to effects of biological interest show that sampling frequency has a larger effect on the bacterial communities than the material does (which we show in Figure 2 is effectively null), but a small effect than the location of the plate in the office or the sequencing run, which we show in Figures 2 and 5 to impact our observed bacterial communities. (c) Similar results are observed with unweighted UniFrac.
Figure 4: Confusion matrices illustrating the performance of city and office SVM classifiers
(a) True (actual) city and predicted city for our SVM classifier when trained and tested on office microbiomes by city. Dark colors on the diagonal indicate the predicted label is very frequently correct. (b) True and predicted office for our SVM classifier when trained and tested on office microbiomes from all cities labeled by individual office. Note that the diagonal is not as dark as in (a), illustrating that the predicted office labels are not correct as often as the predicted city labels. When an incorrect office is predicted, it is often from the correct city, as indicated by the darker colors surrounding the diagonal. (c-e) True and predicted office for our SVM classifiers when trained and tested on office microbiomes from cities individually.
Figure 5: Investigation of sequencing run effect on observed bacterial community composition
(a) Weighted and unweighted UniFrac principal coordinates analysis (PCoA) and Phylogenetic Diversity (PD) by sequencing run for sequencing runs 1, 2 and 3. (b) Weighted and unweighted UniFrac PCoA and PD by season. (c) Weighted and unweighted UniFrac PCoA and PD for samples of the same sites from summer and fall when sequenced in a single sequencing run. (d) Weighted and unweighted UniFrac PCoA and PD of technical replicate samples sequenced on sequencing runs 1, 3, and 4.
Figure 6: Community richness as a function of equilibrium relative humidity of sites for bacterial (16S) and fungal (ITS) communities
We observe weak correlations between community richness and equilibrium relative humidity (ERH) of sites. While these correlations are statistically significant, we do not find these relationships to be convincing for reasons discussed in Results and Supplementary Results.
Figure 7: Human-based source tracking of office microbiome samples
(a) Percent contribution of microbiomes of different sites of the human body to the office bacterial communities. Unknown indicates contribution from a source other than the body sites tested here. (b) Percent contribution of microbiomes of different human subjects to the office bacterial communities. Unknown indicates contribution from a source other than the individuals tested here.
Figure 8: Longitudinal analysis of bacterial Phylogenetic Diversity (PD) over one year
(a) Flagstaff, (b) San Diego, and (c) Toronto. Each “violin” represents a two week period from the beginning, middle, and end of our four sampling periods.[b]
Supplementary Table 1
Descriptions of offices included in this study.
Supplementary Dataset 1
Differentially abundant OTUs across office locations, as determined by ANCOM.
Supplementary Dataset 2
Differentially abundant OTUs across frequently and infrequently sampled sites, as determined by ANCOM.
Supplementary Dataset 3
Differentially abundant OTUs across cities, as determined by ANCOM.
Supplementary Figure 1: Experimental design
Analog of Figure 1, illustrating fungal community results.
Supplementary Figure 2: Fungal community dissimilarity
Analog of Figure 2, illustrating fungal community results.
Supplementary Figure 3: Disturbance due to repeat sampling, though detectable, is small compared to other variables
Analog of Figure 3, illustrating fungal community results.
Supplementary Figure 4: Confusion matrices illustrating the performance of city and office SVM classifiers
Analog of Figure 4, illustrating fungal community results.
Supplementary Figure 5: Investigation of sequencing run effect on observed bacterial community composition
Analog of Figure 5, illustrating fungal community results.