- Open Access
Relative and contextual contribution of different sources to the composition and abundance of indoor air bacteria in residences
© Miletto and Lindow. 2015
Received: 31 August 2015
Accepted: 29 October 2015
Published: 10 December 2015
The study of the microbial communities in the built environment is of critical importance as humans spend the majority of their time indoors. While the microorganisms in living spaces, especially those in the air, can impact health and well-being, little is known of their identity and the processes that determine their assembly. We investigated the source-sink relationships of airborne bacteria in 29 homes in the San Francisco Bay Area. Samples taken in the sites expected to be source habitats for indoor air microbes were analyzed by 16S rRNA-based pyrosequencing and quantitative PCR. The community composition was related to the characteristics of the household collected at the time of sampling, including the number of residents and pets, activity levels, frequency of cooking and vacuum cleaning, extent of natural ventilation, and abundance and type of vegetation surrounding the building.
Indoor air harbored a diverse bacterial community dominated by Diaphorobacter sp., Propionibacterium sp., Sphingomonas sp., and Alicyclobacillus sp. Source-sink analysis suggested that outdoor air was the primary source of indoor air microbes in most homes. Bacterial phylogenetic diversity and relative abundance in indoor air did not differ statistically from that in outdoor air. Moreover, the abundance of bacteria in outdoor air was positively correlated with that in indoor air, as would be expected if outdoor air was the main contributor to the bacterial community in indoor bioaerosols. The number of residents, presence of pets, and local tap water also influenced the diversity and size of indoor air microbes. The bacterial load in air increased with the number of residents, activity, and frequency of natural ventilation, and the proportion of bacteria putatively derived from skin increased with the number of residents. Vacuum cleaning increased the signature of pet- and floor-derived bacteria in indoor air, while the frequency of natural ventilation decreased the relative abundance of tap water-derived microorganisms in air.
Indoor air in residences harbors a diverse bacterial community originating from both outdoor and indoor sources and is strongly influenced by household characteristics.
The study of the indoor microbiome (the microbial communities of the built environment) is of critical importance since humans spend the majority of their time indoors and thus regularly encounter microbes in this habitat. Microbes, including those present in indoor spaces, impact human health and well-being [1, 2]. While initial research in the field of the microbiology of the built environment has mainly focused on microbes of clinical importance, there is growing evidence that a wide variety of microbial taxa are present in the air and on surfaces within buildings. Interrogations of microbial communities have shown that microbes are both diverse and ubiquitous indoors. A diversity of bacteria is found on every surface, especially in kitchens and bathrooms where the environmental conditions are particularly suitable for their immigration and possibly also survival and growth. Bacteria have also been described on pets, indoor plants, foodstuffs, and tap water [3–12]. Bacteria colonize humans, and humans can function as microbial vectors shaping the microbiome of indoor surfaces with which they come in contact [13, 14]. Moreover, dust that has settled on floors and/or carpeting is rich in microbes and, as a complex mixture of inorganic and organic particles, probably represents an integrative record of microbial biodiversity in occupied spaces . Despite demonstrated microbial ubiquity, various microbial taxa exhibit biogeography patterns indoors as well as outdoors [10, 16, 17], suggesting that they are subject to limitations on dispersal and are derived from local sources. In addition, the variation in environmental conditions within buildings (such as humidity, temperature, and availability of nutrients), the intensity or legacy of human usage (e.g., cleaning patterns), and the variation in possible source habitats (e.g., human numbers and features, pets, plants, food, and tap water) are likely to shape the indoor microbiome creating microhabitats colonized by distinct microbial communities.
Air represents a vehicle for movement of microbes from one habitat to another. From indoor sites, microorganisms can directly enter the aerosol phase, such as through the shedding of bacterial-colonized skin cells  or the aerosolization of saliva and tap water, and indirectly through the resuspension of settled dust. Irrespective of the means by which they enter the air, they likely represent also an important component of the microbes to which we are exposed  given that humans inhale about 10–25 m3 of air a day . It would be expected that the contribution of various sources of bacteria to the composition and abundance of indoor air bacteria would be directly related to both their numbers in a given source habitat and the ease by which they enter the air. For example, the relative abundance of human-associated microbes in indoor air increases with the number of residents [21, 22]. Other factors that might influence the contribution of different sources of microbes to the air include human activity levels as well as the frequency of cooking, vacuum cleaning, and showering [23–25]. While there have been many studies of the microbial communities present on humans and the many surfaces within buildings with which they might interact, few studies have attempted to study the relative importance of these various sources on the composition of indoor air, and thus to address issues such as the efficiency of immigration of microbes into the air within buildings. This will be the focus of our study.
Since homes constitute a barrier separating living spaces from the outside world, the degree of exchange between the outdoor microbiome and the indoor microbiome is central to understanding source-sink relationships for indoor air bacterial communities. Outdoor air has been shown to contribute to the indoor air fungal microbiome . Given that microbial communities in outdoor air are both abundant and subject to substantial biogeographic variation [27, 28], it seems likely that they could contribute to the variation in composition or abundance of microbes in indoor air, although this has not been well studied. The biogeographical patterns of airborne bacteria, in turn, are likely driven by spatial patterns of vegetation since the large populations of bacteria on the leaf surface are probably substantial contributors to the air microflora outdoors . It would be expected that the mode of ventilation of a building would influence the composition of airborne bacteria communities indoors. In support of this concept, the phylogenetic diversity of indoor air was found to be lower than that outdoors while the proportion of microorganisms likely originating from indoor sources is higher in indoor air than outdoor air, especially in mechanically ventilated rooms rather than in window-ventilated rooms . While there is a growing number of studies which have addressed the airborne microflora within the built environment, most of these studies have studied institutional buildings such as classrooms or other public spaces such as commercial centers and transit systems which often harbor large number of humans . Furthermore, the study sites are often subject to extensive mechanical ventilation associated with heating and air conditioning systems, which would be expected to strongly impact the abundance and perhaps the identity of microbes found in the air at such sites. Unfortunately, few studies have been made of the airborne microflora in residences which would typically (1) lack the extensive air handling systems present in institutional or commercial buildings, (2) harbor a much lower spatial density of occupants, and which (3) might exhibit airborne communities that are likely distinctive due to their location and the activity of the human occupants.
To better understand the relative importance of various sources of bacteria on the microbial composition of indoor air in residences and thus determine the extent of idiosyncrasy of the microbiome within buildings, we performed an intensive analysis of 29 homes located in the San Francisco Bay Area using 16S rRNA-based pyrosequencing and quantitative PCR. In each residence, samples were collected from indoor air and from a variety of indoor and outdoor sites expected to be likely sources of airborne microbes (kitchen countertops, refrigerator shelves, showerheads, toilet bowls, bathtub tiles, floors, carpeting, residents’ skin, residents’ saliva, pets, tap water, doorsteps, and outdoor air). Considering indoor air to be a “sink” populated by various indoor and outdoor sources, special attention was placed on the role and magnitude of immigration of microbes from outdoor air to the interior of residences; these two air parcels were sampled simultaneously immediately prior to sampling all other sites, and their microbial composition compared at a given residence. Information on the characteristics of the household was collected at the time of sampling including the following: number of residents, activity level, frequency of cooking, frequency of vacuum cleaning, number and type of pets, frequency of natural ventilation, surrounding vegetation coverage, time of sampling, sky cloud coverage, wind, air temperature, and air humidity. While outdoor air was frequently found to be a major source of bacteria found within indoor air, the relative contribution of this and other sources to the indoor air microbiome was strongly influenced by household characteristics and the behavior of the residents.
Results and discussion
Microbial community composition in indoor air
Sequences affiliated with Propionibacterium (12 % in indoor air), representative genus of the family Propionibacteriaceae, were in the highest proportion in outdoor air, skin, pets, carpet, bathtub tiles, and tap water samples. A similar occurrence in the source environments analyzed was observed for Corynebacterium (4 %) and Staphylococcus (4 %). These microorganisms typically colonize the skin of humans and other organisms, as well as Acinetobacter (2 %) and Kocuria (1 %) that, in contrast with Propionibacterium, Corynebacterium, and Staphylococcus, are mainly found on kitchen countertops and refrigerator samples (Additional file 2: Table S2).
Several of these genera have been observed in both outdoor and indoor air in previous studies (Additional file 2: Table S2). Air is an inhospitable environment for microbes and little or no growth is expected, although there is a growing evidence that microorganisms may exhibit metabolic activity while in the airborne state . Nevertheless, traits for enhanced survival in air are necessary if viable cells are to be successfully dispersed to other sites, and many of the OTUs we found in air are related to microorganisms that are capable to resist to desiccation and temperature fluctuations, produce spores, are oligotrophic and/or metabolically versatile, or possess mechanisms to protect from cellular damage due to the exposition to electromagnetic radiation (e.g., the production of pigments and efficient DNA repair systems; Additional file 2: Table S2). Even human-associated microbes (skin and gut) can remain viable in air for many hours after dispersal, as shown in a recent study focused on their ecological succession on restroom surfaces .
Microbial community composition in various source environments
Assignment of origin of indoor air constituents by microbial source tracking
Outdoor air is the primary source of bacteria in indoor air in these residences
Effect of the household characteristics on the abundance and diversity of bacteria in air
Effect of the household characteristics on the abundance and diversity of bacteria in air
Number of residents
Number of pets
Indoor air harbors a diverse collection of bacteria originating from both indoor and outdoor sources. While bacteria are present on many surfaces within residences, they are apparently not efficiently introduced into the air from most of such sites, as the composition of bacteria communities in the air did not resemble that of most inanimate surfaces. In contrast, the escape of bacteria from tap water to interior air appears to be more efficient than from surfaces. While humans themselves and their pets can be substantial sources of bacteria that enter indoor air in residences, their contribution is influenced by the number and activity of the residents. Given that the concentration of bacteria in outdoor air was usually higher than that of indoor air and ventilation such as by opening doors and windows could efficiently replace indoor air with that from external sites, outdoor air was a major contributor of bacteria to the residences studied.
Sites and sample collections
Sampling of 29 single-family homes or individual apartment units in multiunit buildings located in the San Francisco Bay Area, California, took place between April and May 2013. In each residence, surface samples were collected a single time from 12 sites (indoor sources): kitchen countertop, refrigerator shelves, showerhead, toilet bowl, bathtub tiles, floor (tiles, wood, linoleum), carpet (fitted carpeting, rug), residents’ skin (forehead), residents’ saliva, pet (fur, scales, feathers, or designated pet area e.g., cage), tap water, and doorstep. In addition, outdoor air (outdoor source) and indoor air (considered a sink) was simultaneously sampled at each site. These surface sampling sites were present in most residences allowing enough replication for downstream statistical analyses (most source environments, n = 29; floor, n = 19; carpet, n = 17; pet, n = 12). These sites were chosen because they were expected to be locations of the highest microbial abundance and/or were considered to be most likely to be aerosolized. For six of the residences, selected based on volunteer availability, indoor and outdoor air samples were collected daily for seven consecutive days. Indoor air and outdoor air were sampled simultaneously prior to collecting any other sample. No residents were at home during aerosol sampling. While sampling was not done at the same time of day for each residence, all samples were collected during daylight hours and soon after the residents had left their homes. It was therefore expected that contributions from resident activities on airborne microflora was proportional to their numbers or behaviors and not strongly influenced by sampling time itself. For indoor sampling, the filtration apparatus was placed in the middle of the living room (central position within each residence) and consisted of a sterile filter cassette equipped with a 0.22-μm cellulose nitrate filter (Fisher Scientific, Pittsburgh, PA, USA) suspended 1 m above the floor by means of a tripod and connected with tubing to a vacuum pump (High Output Vacuum/Pressure Pump; Millipore, Billerica, MA, USA). Outdoor samples were taken simultaneously by a similar method at a site within about 5 m of the entry to each residence. Approximately 1 m3 of air was filtered by operating the pump at a constant vacuum/flow rate over a period of 1 h. Filters were sealed and stored frozen at −20 °C until DNA extraction. Tap water (50 mL) was collected after flushing of plumbing for 2 min from a bathroom faucet in a sterile tube and stored on ice until returning to the lab. There, water was immediately filtered through a 0.22 μm cellulose nitrate filter, and the filter was sealed and stored at −20 °C until processing. Surfaces were sampled for 10 s using sterile, cotton-tipped swabs. Immediately after sampling, the tip of the sampling swab was excised directly into a PowerSoil® Bead Tube (PowerSoil®DNA Isolation Kit; MoBio, Solana Beach, CA, USA) and stored on ice until return to the lab. For each household, skin and saliva swabs taken from each resident were pooled to protect volunteer confidentiality. Genomic DNA was extracted from the swabs on the same day of sampling, while air and water samples (filters) were processed at the end of the sampling campaign. Information on the characteristics of the household was collected at the time of sampling including the following: number of residents, activity level (average hours spent at home daily), frequency of cooking (times per week), frequency of vacuum cleaning (times per month), number and type of pets, frequency of natural ventilation (average hours daily), and surrounding vegetation coverage (Additional file 5: Table S4). Additional information included: time of sampling, sky cloud coverage, wind (http://www.wunderground.com), air temperature, and humidity (HOBO T/Rh data logger; Onset Computer Corp., Bourne, MA, USA). The sampling protocol was approved by the University of California Committee for the Protection of Human Subjects (protocol ID #2011-03-2947).
Filters were thawed and sliced aseptically in a DNA-free work area, using a razor blade treated by immersion in DNA AWAY™ (Thermo Scientific, Waltham, MA, USA) and ethanol and then flamed prior to use. Filter segments were loaded into a PowerSoil® Bead Tube (MoBio, Solana Beach, CA, USA). Tubes were processed according to the manufacturer’s protocol, following 30 s of beating at maximum speed after the addition of solution C1 to the PowerSoil® Bead Tube. A total of 437 DNA samples were extracted. Extraction controls were processed to exclude the presence of contaminations on reagents (no sample), filters (sterile filter), and swabs (sterile swab).
Approximately 381 bp from the 16S rRNA hypervariable region V2 was amplified from each DNA sample in triplicates and pooled. Each sample was amplified with a unique barcode to enable multiplexing in the 454 runs. Fusion primers for unidirectional sequencing (Lib-L) were designed from primers UNIV27F (5′-AGAGTTTGATCCTGGCTCAG-3′) and BACT338R (5′-TGCTGCCTCCCGTAGGAGT-3′) according to Roche guidelines. In particular, forward fusion primer included the barcode (MID1-100; Roche Molecular Diagnostics, Pleasanton, CA, USA) and primer BACT338R to obtain a good read of the target region. Negative controls were included in each PCR assays. The reaction mixture contained 5 μL of genomic DNA extract, 0.5 μL of each fusion primer (30 μM), 20 μL of 5 PRIME MasterMix (1×), and 24 μL of PCR quality water. Thermal cycling conditions were the following: 3 min at 94 °C, followed by 30 cycles of 45 s at 94 °C, 30 s at 50 °C, and 1.5 min at 72 °C. The cycling was completed by a final elongation step at 72 °C for 10 min. The post-PCR cleanup was performed with magnetic beads (Agencourt® AMPure® XP PCR Purification System; Agencourt Bioscience Corporation, Beverly, MA, USA). Amplicons were quantified using the Invitrogen Qubit™ dsDNA HS Assay Kit (Invitrogen, Carlsbad, CA, USA) and multiplexed at an equimolar concentration (10 ng/μL). Negative extraction controls did not yield enough amplicons for sequencing. Samples (434) were split in 17 libraries and sequenced on a Roche GS FLX+ System at the University of Illinois (W.M. Keck Center for Comparative and Functional Genomics, University of Illinois, Urbana-Champaign). Information on all the processed samples is detailed in the mapping file (Additional file 6: Table S5).
All sequences generated for this study were processed using the default parameters in QIIME . In brief, demultiplexing included a quality filter (minimum quality score of 25), the removal of the reverse primer and any sequence from the end of each read, and a length filter (min = 300 bp, max = 400 bp). After quality control and barcode assignment, the remaining high-quality reads (867,567) from each run were merged in a single fasta file. Sequences were binned into OTUs at a 97 % sequence similarity cutoff using UCLUST . Representative sequences for each OTU were assigned taxonomy with the RDP Classifier  and aligned using PyNAST  against the Greengenes core set . Chimeric sequences were identified with ChimeraSlayer  and removed from the database, as well as singletons, phylotypes classified as chloroplasts and mitochondria, and OTUs present in less than 1 % of the samples. Samples were rarefied to 100 per sample to eliminate potential biases introduced by uneven sampling depth. Samples with fewer than 100 sequences were excluded from taxonomic, alpha-diversity, beta-diversity, and source tracking analyses.
Data analysis relied on the software QIIME and R . The qualitative (unweighted) and qualitative (weighted) UniFrac metrics were used to determine the phylogenetic distance of the bacterial communities associated with indoor air and the putative sources. Distances were visualized on a nonmetric multidimensional scaling (NMDS) plot, and the statistical significance of similarity between indoor air and sources was analyzed with ANOSIM available in the vegan R package (999 permutations, ). The SourceTracker software package  was used to determine the potential sources of bacteria in indoor air and their importance in the households sampled. Phylogenetic diversity (Faith’s PD) for indoor and outdoor air was calculated with the pd function in the picante package in R . Mantel test (999 permutations) was used to test the correlation between the taxonomic distance matrix built from indoor and outdoor bacterial community composition data (UniFrac), the geographic distance, and the distance between the households calculated from abundance data of bacteria in indoor and outdoor air using Bray-Curtis dissimilarity metrics. The Spearman’s rank correlation coefficient (ρ) was used to measure the strength of the relationship between the sets of data produced in this study. The Student’s t test was used to determine if indoor air and outdoor air are significantly different from each other based on Faith’s PD and bacterial abundance.
Bacterial abundance was determined by qualitative PCR (qPCR) in polypropylene 96-well plates on a 7500 Real-Time PCR System (Applied Biosystems Inc., Foster City, CA, USA). A 16S rRNA fragment of approximately 180 bp was amplified using primers EUB338F (5′-ACTCCTACGGGAGGCAGCAG-3′; ) and EUB518R (5′-ATTACCGCGGCTGCTGG-3′; ) following a protocol previously described . Briefly, each 25-μL qPCR mixture contained 12.5 μL of Power SYBR green PCR Master Mix (Applied Biosystems Inc., Foster City, CA, USA), 1.5 μL of a 150-nM concentration of each primer, and 9.5 μL of a 1:10 dilution of genomic DNA template. PCR conditions were as follows: 15 min at 95 °C followed by 40 cycles of 1 min at 95 °C, 30 s at 55 °C, and 1 min at 72 °C. Standard curves were constructed with serial dilutions of known amounts of 16S RNA genes amplified with primers EUB338F and EUB518R from environmental genomic DNA. Serial dilutions covered a range of 8 orders of magnitude of template copies per assay (102 to 109). R 2 values ranged from 0.996 to 0.999. The qPCR efficiency (97 to 100 %) was calculated based on the slope of the standard curve. All qPCR assays were run in triplicates. Melting curve analysis of the qPCR products was conducted for each assay to confirm the specificity of the qPCR assays. Target copy numbers for each reaction were calculated from the standard curves assuming an average molar mass of a DNA base pair of 660 g mol−1. Correct amplicon size was verified by running aliquots of qPCR on an ethidium bromide-stained 1 % agarose gel. Genomic DNA extracts were tested for PCR-inhibitory substances running qPCR assays on a serial dilution of the template genomic DNA. Templates were normalized to an equal amount of genomic DNA to enable comparison of results.
Availability of supporting data
The sequence data set supporting the results of this article is available in the FigShare repository [10.6084/m9.figshare.1525083]. All additional files supporting the results of this article are included within the article and its additional files.
This work was enabled by the generous support of the Alfred P. Sloan Foundation (http://www.sloan.org; grant number 2010-10-03). The funders had no role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. We thank all those that kindly volunteered their homes during the sampling campaign.
Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
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