bioRxiv preprint doi: https://doi.org/10.1101/413773; this version posted September 11, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC 4.0 International license.
1 Lessons learned when looking for non-neutral ecological processes in the built 2 environment: the bacterial and fungal microbiota of shower tiles 3 4 Rachel I. Adams and Despoina L. Lymperopoulou 5 Plant & Microbial Biology, University of California, Berkeley, California, USA 6 7 Corresponding author: Rachel Adams, [email protected] 8 9 Abstract 10 With periodic pulses of water, bathroom showers represent a habitat in the built 11 environment with a high potential for microbial growth. We set out to apply a neutral model of 12 microbial community assembly and to identify deviations from the model that would indicate 13 non-neutral dynamics, such as selective pressures for individual taxa, in this particular indoor 14 habitat. Following a cleaning event, the bacterial and fungal microbiota of the shower stalls in 15 two residences in the San Francisco Bay Area were observed over a four-week period. We 16 observed strong differences in composition between houses, preventing us from combining 17 samples and thus limiting our statistical power. We also identified different aspects of the 18 sampling scheme that could be improved, including increasing the sampling area (to ensure 19 sufficient biomass) and increasing the number of replicates within an individual shower. The 20 data from this pilot study indicate that immigrants to the built environment arising from human 21 shedding dominate the shower ecosystem and that growth conditions are relatively 22 unfavorable despite the water availability. We offer suggestions on how to improve the 23 studying and sampling of microbes in indoor environments. 24 25 Introduction 26 In the built environment of human residences, water is generally a limiting resource. 27 Built to remain dry, most areas of homes are expected to be ecological sinks (as defined by 28 Pulliam 1988), where there is a greater influence of dispersal into the system than present from 29 endogenous growth. An exception to this source-sink framework in homes may be those areas 30 in the bathrooms and kitchens, which receive intentional and frequent water use. These areas 31 where nutrients and periodic pulses of water are available may be ecologically productive, such
1 bioRxiv preprint doi: https://doi.org/10.1101/413773; this version posted September 11, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC 4.0 International license.
32 that microbial communities can become established and interact with each other and their 33 environment; in other words, these wet areas may be true biological ecosystems. 34 We hypothesized that bathroom showers would be a microbial ecosystem, and so we 35 set out to explore change in the bacterial and fungal communities over time in the built 36 environment. We were inspired by recent work exploring neutral and non-neutral processes 37 that drive microbial assembly in the guts of zebrafish (Burns et al. 2016). As a parallel to 38 zebrafish age, or “days post fertilization”, we considered time since cleaning, which at its best is 39 a process that severely disturbs the established community, eliminating a large portion of 40 existing biomass and creating open niches. 41 Previous work has shown that both dispersal and selective pressures determine the 42 bacterial composition of bathrooms. Flores et al. (2011) showed that surfaces generally 43 clustered into three types based on their dominant bacterial source populations: surfaces 44 routinely touched with hands, the restroom floor, and toilet surfaces. In a high-occupancy 45 university bathroom setting, the bacterial composition shifted throughout an 8-hour day to one 46 dominated by skin-associated taxa and after this short period, surfaces were compositionally 47 similar to those surfaces that were left for one month prior to sampling (Gibbons et al. 2015). In 48 contrast to surfaces that are exposed to air and human occupants, structures that are part of 49 the plumbing system, such as showerheads and hospital therapy pools, are dominated by a 50 common group of taxa that includes Mycobacterium, Methylobacterium, and Sphingomonas 51 (Angenent et al. 2005; Feazel et al. 2009; Perkins et al. 2009; Miletto & Lindow 2015). Less work 52 has been done examining fungi on bathroom surfaces, although Exophiala, Fusarium, and 53 Rhodotorula are common components of drains and dishwashers (Zalar et al. 2011; Adams et 54 al. 2013; Zupančič et al. 2016). 55 The Sloan neutral model in microbial community predicts the relationship between the 56 occurrence frequency of a taxon in individual local communities and its abundance in the 57 overall metacommunity (Sloan et al. 2006). Using the non-linear least squares as a model of 58 neutral dynamics, one aspect that can be teased out from this application is what taxa deviate 59 from this null model. Those taxa that are more prevalent (have a higher occurrence frequency) 60 than expected based on the model are either being selected for or are frequent immigrants into
2 bioRxiv preprint doi: https://doi.org/10.1101/413773; this version posted September 11, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC 4.0 International license.
61 the system. Those taxa that are less prevalent than predicted based on their overall abundance 62 are either selected against in the system or are dispersal limited. 63 In the shower tiles of residential bathrooms, the source communities are airborne, 64 waterborne, and human-associated. Once present, the habitat of bathroom tiles has strong 65 selective pressures resulting from relatively unfavorable growth conditions, including 66 desiccation periods and nutrients that are both limited and specific. Nevertheless, even the 67 periodic application of water could create favorable conditions, and we hypothesized that we 68 would be able to, by applying ecological theory, identify those taxa that did not follow neutral 69 processes marked by stochastic loss and replacement of individuals. Ultimately, we discovered 70 that the sampling scheme would have to be highly tailored to this specific environment and 71 proceed for a lengthy duration between cleanings that many would find undesirable for home 72 environments. 73 74 Materials and Methods 75 Sample collection 76 Shower stalls in two houses in the San Francisco Bay Area were sampled for four 77 consecutive weeks in February and March, 2018. Two areas of the shower were targeted, one 78 at the back of the shower opposite the showerhead, and another on the side wall of the shower 79 (Figure 1). Each area was approximately 52 cm wide and 26 cm high and partitioned into four 80 equal size 13 cm x 26 cm subsections. At the start of the sampling period, surfaces were 81 cleaned according to the practices of the house. In House 1, the surface was sprayed with a 82 “disinfecting bathroom bleach-free cleaner”, wiped with a sponge, rinsed with water, dried out 83 with a washcloth, and finally cleaned with an ethanol wipe. In House 2, the surface was sprayed 84 with a “naturally derived tub and tile cleaner” and washed with a washcloth for approximately 85 30 seconds. 86 Approximately one week after cleaning, the first partition was sampled by rubbing a 87 water-moistened Floq swab back-and-forth over the first subsection area for approximately one 88 minute. The swab tip was broken off into an Eppendorf tube and immediately frozen. 89 Approximately two weeks after cleaning, one swab was used to sample the subsection that had
3 bioRxiv preprint doi: https://doi.org/10.1101/413773; this version posted September 11, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC 4.0 International license.
90 been sampled the previous week and a second swab was used to sample the next subsection 91 that had been left undisturbed since cleaning. This continued for four weeks and resulted in 10 92 swabs for a given area of the shower stall, either back or side wall. Sampling was approved by 93 the University of California Committee for the Protection of Human Subjects under protocol 94 2015-02-7135. 95
96 97 Figure 1. Image of one of the shower stalls studied, highlighting that two different areas of the stall were targeted. 98 99 Sequence generation and analysis 100 Genomic material in swabs were extracted using the Qiagen PowerSoil Kit and followed 101 the manufacturer protocol. Libraries were prepared at the QB3 Vincent J. Coates Genomics 102 Sequencing Laboratory at the University of California, Berkeley, using a two-step PCR process 103 (Detailed methods are included in Supplementary File 1). For bacteria, the 16S v4 hypervariable 104 gene region was targeted using the 515f forward primer and 806r reverse primer in the first 105 PCR. For fungi, the first PCR targeted the ITS1 region of the 18S gene using the ITS1f forward 106 primer and ITS2 reverse primer. The second PCR reaction adds Illumina adapters and barcodes 107 to either side of the PCR products. Both positive and negative samples were included. For 108 bacteria, we purchased a mock microbial community standard (Zymo Research). For fungi, non- 109 biological synthetic mocks were cloned and combined into a community (Palmer et al. 2018). 110 Extraction and PCR negatives were also processed along with our biological samples. All the 111 samples were pooled and run on a single 300PE v3 MiSeq lane. Sequences have been deposited 112 in NCBI’s Sequence Read Archive (SRA) with accession SRP148593.
4 bioRxiv preprint doi: https://doi.org/10.1101/413773; this version posted September 11, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC 4.0 International license.
113 Bacteria sequenced were processed using the DADA2 pipeline (Callahan et al. 2016) 114 implemented in R (R Development Core Team 2014). Detailed commands are included in the 115 Supplementary file and briefly discussed here. Forward and reverse reads were filtered and 116 trimmed, and the error rates learned. Following dereplication, amplicon sequence variants 117 (ASVs) were inferred on the pooled data. Forward and reverse reads were then paired, and a 118 table was constructed from the merged reads. Chimeras were removed, and taxonomy 119 assigned against the SILVA database (Quast et al. 2013). 120 Fungal sequences were processed using AMPtk v1.0.2 (Palmer et al. 2018). Reads were 121 preprocessed, specifying the ITS1-F and ITS2 primers were not requiring. This program relies on 122 internal scripts as well as usearch (Edgar 2010) and vsearch (Rognes et al. 2016) to merge 123 paired reads, remove PhiX, quality filter, trim any remaining primer sequence, cluster 124 sequences, and remove chimeras. Taxonomy was assigned using a combination of sintax (Edgar 125 2016) and utax against the UNITE database (28.06.2017) (Koljalg et al. 2005), with the 126 sequences of the synthetic mock added. Detailed commands are included in the Supplementary 127 file. 128 Community tables were analyzed using the Phyloseq package (McMurdie & Holmes 129 2013) in R (R Development Core Team 2014). Contaminant sequences were identified using the 130 decontam package (Davis et al. 2017) at the 50% prevalence threshold for bacteria and 30% 131 threshold for fungi. The program identified 112 bacterial contaminants and 21 fungal 132 contaminants, and these were removed from their respective community tables. Taxa that 133 were classified as Eukaryota (n = 83) or left unclassified (Phylum=NA, n=34) were removed from 134 the bacteria community table. Assessment of the performance of the positive mocks were 135 evaluated following these filtering steps. For the bacteria mock, the eight bacteria taxa were 136 recovered at high sequence read count (>10,000 sequences), with an additional 52 taxa 137 represented at lower sequence count (< 2,600). Most of these were not present in study 138 samples, and, based on their shared taxonomic identity with those in the mock, likely resulted 139 from imprecise grouping of mock sequences. With the fungal mock, the eight synthetic mock 140 sequences were recovered at high sequence read count (> 25,000 sequences), with an 141 additional seven taxa represented at low sequences abundance (< 11 reads).
5 bioRxiv preprint doi: https://doi.org/10.1101/413773; this version posted September 11, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC 4.0 International license.
142 143 Results and Discussion 144 We frame our results in the form of main findings and lessons learned in order to inform 145 future efforts when looking for non-neutral ecological processes in built environments. 146 1. Houses are not zebrafish 147 We approached this work viewing houses as technical replicates, as was done in 148 previous work where separate buildings are sampled in order to identify patterns common 149 across the different settings (e.g. Flores et al. 2011; Dunn et al. 2013; Gibbons et al. 2015). We 150 observed strong differences in the microbial communities across these two houses, particularly 151 for fungi (Figure 2). House 2 was dominated by two fungi, Didymella glomerata and 152 Filobasidium magnum, and this stayed constant throughout the sampling period. In contrast, 153 the fungal community in House 1 was much more diverse (Shannon diversity, mean House 1 = 154 2.5; mean House 2 = 0.64; t-test p<0.001). Using the Bray distance index, house alone explained 155 44% of the variance between fungal taxa (adonis p<0.001). Bacteria showed a similar pattern of 156 lower diversity in House 2, but the difference was less extreme (Shannon diversity, mean House 157 1 = 4.3; mean House 2 = 3.7; t-test p<0.001). Again, using the Bray distance index, house 158 explained 30% of the variance between bacterial taxa (adonis p<0.001). The strength of the 159 house effects are consistent with another study looking at the bacteria on kitchen sinks (Moen 160 et al. 2016). 161 The strong microbial communities between houses required that investigations in 162 neutral and non-neutral processes occured within an individual house. That is, unlike zebrafish, 163 the samples from different houses could not be combined into a common dataset. This strong 164 house-specific effect is not altogether surprising, given the many ecological factors that could 165 bring about this pattern, notably founder effects and different source populations arising from 166 geographic and plumbing system variation. Nevertheless, efforts to characterize the microbiota 167 in buildings typically draw on many different houses. For example, the 1000 Homes Project was 168 a community-based sampling effort across the entire United States; it showed that the bacteria 169 in house dust were largely determined by occupants and their activities, while fungi were 170 largely determined by outdoor environmental characteristics (Barberán et al. 2015a; Barberán
6 bioRxiv preprint doi: https://doi.org/10.1101/413773; this version posted September 11, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC 4.0 International license.
171 et al. 2015b; Grantham et al. 2015). On the other hand, a study looking at the interactions 172 between human occupants and dry surfaces in indoor environments (such as doorknobs, light 173 switches, and floors) focused on repeated sampling of six homes (Lax et al. 2014). A larger 174 version of this pilot study would benefit from this second kind of sampling scheme, in that it 175 balances representation across homes with sufficient sampling depth to investigation temporal 176 dynamics.
Order
Agaricales Holtermanniales 15000 Boletales Hypocreales Cantharellales Malasseziales Capnodiales Pleosporales Chaetothyriales Polyporales 10000 Curvibasidium Russulales Cystobasidiales Saccharomycetales
Abundance Dothideales Sporidiobolales Eurotiales Tremellales 5000 Filobasidiales uncultured_fungus Geomyces Venturiales Gloeophyllales Wallemiales Helotiales Xylariales 0 House1W0side House1W1T1back House1W1T1side House1W2T1back House1W2T1side House1W2T2back House1W2T2side House1W3T1back House1W3T1side House1W3T2back House1W3T2side House1W3T3back House1W3T3side House1W4T1back House1W4T1side House1W4T2back House1W4T2side House1W4T3back House1W4T3side House1W4T4back House1W4T4side House2W0back House2W0side House2W1T1back House2W1T1side House2W2T1back House2W2T1side House2W2T2back House2W2T2side House2W3T1Back House2W3T1Side House2W3T2Back House2W3T2Side House2W3T3Back House2W3T3Side House2W4T1Back House2W4T1Side House2W4T2Back House2W4T2Side House2W4T3Back House2W4T3Side House2W4T4Back House2W4T4Side
177 Sample 178 Figure 2. Overview of fungal orders between the two houses. Samples are named according to house (House 1 or 179 House 2), the week it was sampled (W0-W4), the tile that was sampled (T1-T4), and the location in the shower 180 (back or side). 181 182 2. Identifying representative sampling 183 Spatial variation within a specific surface type in the home environment has not been 184 systematically studied. Spatial structure could arise if the orientation of the stall and occupant 185 create differential source strengths of the water from the premise plumbing system and what 186 splashes off the occupants. In a previous study, we found that variation in community 187 composition across two different areas of a kitchen sink and a shower stall was less than the 188 variation observed across homes (Adams et al. 2017). In agreement with our previous 189 observation, we found that, within a house, there was little or no impact of location (back or 190 side) within the shower on fungal composition (adonis, House 1: R2=0.07, p=0.01; House 2:
7 bioRxiv preprint doi: https://doi.org/10.1101/413773; this version posted September 11, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC 4.0 International license.
191 R2=0.003, p=0.87), but a stronger effect of location for the bacterial composition (adonis, House 192 1: R2=0.13, p=0.01; House 2: R2=0.21, p=0.001). Given these results, the value of replicates 193 exceeds the potential information gleaned from spatial structure. Thus, many different areas of 194 the shower stall should be sampled at a given time point, and these can serve as time point 195 replicates in the data analysis stage. 196 Swabbing surfaces should represent a disturbance to the microbiota assembled there, so 197 we sampled a new tile each week as well as the tiles sampled previously (for example, in week 198 3, we sampled tile 3, as well as took repeat swabs of tiles 1 and 2 that had been swabbed in 199 previous weeks). In the analysis, these samples of re-swabbed areas provided little, if any, 200 information on the dynamics of the system. The dominant taxa remained consistent across the 201 weeks, regardless of how many times that area had been swabbed, and it was low abundant 202 taxa that differed across weeks. Swabbing was just one of many factors that could have caused 203 the fluctuation of low abundant taxa. Other factors that could affect low abundant taxa include 204 changes in the waterborne taxa introduced into the shower and stochastic splashing off of the 205 shower occupants. Our study was not able to tease out the specific effect of swabbing or these 206 other factors. Thus, we conclude that resampling previously swabbed areas can be skipped in 207 future sampling efforts. 208 Sufficient biomass collection in indoor environments is a long-standing problem. While 209 some studies report community profiles from what seems like small biomass, such as from 210 keyboards (Fierer et al. 2010) and light switches (Lax et al. 2014), we found that the yield from 211 the area we sampled was approaching the detection limits of our approach. The reads counts of 212 some of the samples approximated those of some of the negative controls (Figure 3). Recent 213 work shows that including more precise positive controls can help with interpretation of these 214 kinds of low biomass samples (Minich et al. 2018).
8 bioRxiv preprint doi: https://doi.org/10.1101/413773; this version posted September 11, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC 4.0 International license.
Bacteria Fungi 250,000 500,000
200,000 400,000
150,000 300,000
100,000 200,000 LibrarySize LibrarySize
50,000 100,000
0 0 0 20 40 0 20 40 Index Index Sample Type
Negative Control Positive Control 215 Shower Sample 216 Figure 3. Sequence counts of bacterial and fungal samples following removal of taxa using the “decontam” package 217 showing that biomass for many shower samples approximate those of some of the negative controls. 218 219 3. Results of Sloan model and study duration 220 Due to our limited sample size, results from applying a neutral model of community 221 dynamics should be taken with caution. While we had eight samples for the week 4 time point, 222 Burns et al. used approximately 20 fish for each time point. Many bacteria were less prevalent 223 than predicted based on their abundance (bottom right areas in Figure 4); these are either 224 selected against, or are dispersal limited system. In both houses, partitions below the neutral 225 prediction were most strongly distinguished by the presence of Proteobacteria and Firmicutes, 226 in particular the family Sphingomonadaceae and order Clostridiales. There were many more 227 bacteria that were more prevalent than predicted by the model (top left in Figure 4); these are 228 either selected for or are frequent immigrants into the system. Partitions above the neutral 229 prediction were distinguished by Actinobacteria and to a lesser extent Proteobacteria, and 230 were particularly represented by Staphylococcaceae, Streptococcaceae, and Corynebacteriacea 231 (taxa are commonly associated with human skin (Byrd et al. 2018)), and by Mycobacteriaceae 232 and Methylobacteriaceae (taxa associated with premise plumbing (Falkinham et al. 2015)). 233 Similarly, skin-associated taxa were the most common type of bacteria to increase in 234 abundance over the sampling period (Figure 5). 235
9 bioRxiv preprint doi: https://doi.org/10.1101/413773; this version posted September 11, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC 4.0 International license.
House 1 House 2
1.00 1.00
0.75 0.75
0.50 0.50 Prevalence Prevalence
0.25 0.25
0.00 0.00
-10 -5 -15 -10 -5 236 Log abundance Log abundance 237 Figure 4. Individual ASVs of bacteria in the 4th week of sampling and their fit with the Sloan neutral model. 238 239
House 1
Acinetobacter_ASV27 Acinetobacter_ASV29 Aeromicrobium_ASV20 Aeromicrobium_ASV25 Citricoccus 15000 10000 6000 10000 10000 10000 7500 4000 5000 5000 5000 5000 2500 2000 0 0 0 0 0 Location 0 1 2 3 4 0 1 2 3 4 0 1 2 3 4 0 1 2 3 4 0 1 2 3 4 Back Corynebacterium_1 Finegoldia Lawsonella Sneathia Staphylococcus 15000 15000 12500 Side Abundance 10000 10000 10000 10000 7500 40000 5000 5000 20000 5000 5000 2500 0 0 0 0 0 0 1 2 3 4 0 1 2 3 4 0 1 2 3 4 0 1 2 3 4 0 1 2 3 4 Week House 2
Chloroplast Corynebacterium_1 Janibacter Lawsonella Methylobacterium_ASV11 20000 20000 15000 20000 15000 15000 40000 20000 10000 10000 10000 20000 10000 5000 5000 5000 0 0 0 0 0 Location 0 1 2 3 4 0 1 2 3 4 0 1 2 3 4 0 1 2 3 4 0 1 2 3 4 Back Methylobacterium_ASV6 Staphylococcus Streptococcus_ASV2 Streptococcus_ASV5 Streptococcus_ASV7 30000 Side 60000 40000 Abundance 15000 30000 20000 40000 20000 10000 20000 10000 10000 5000 20000 10000 0 0 0 0 0 0 1 2 3 4 0 1 2 3 4 0 1 2 3 4 0 1 2 3 4 0 1 2 3 4 240 Week 241 Figure 5. The change in abundance of the top most abundant bacteria ASVs across the sampling period. 242 243 There were many fewer fungal taxa flagged as deviating from the null model in 244 household showers (Figure 6). Taxa lower than predicted (i.e. selected against or dispersal 245 limited) were frequently taxa associated with outdoor environments, including plant pathogens 246 (Gibberella baccata (Afanide et al. 1976) in House 2) and soil fungi (Fusarium oxysporum
10 bioRxiv preprint doi: https://doi.org/10.1101/413773; this version posted September 11, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC 4.0 International license.
247 (Michielse & Rep 2009) and Papiliotrema terrestris (Liu et al. 2015) in House 1). Fungal taxa 248 that were more prevalent that predicted by the model, based on their abundance, were 249 overwhelmingly members of the Malasseziales order, indicating that this skin-associated fungus 250 (Findley et al. 2013) is a frequent immigrant into this system. Accordingly, the abundance of 251 Malassezia does increase over time (Figure 7). Other fungi, such as Filobasidium, Rhodotorula, 252 Cladosporium, and Papiliotrema, also show increasing abundance over time, highlight which 253 fungal taxa are likely to become increasingly important components of the system should the 254 environment be left undisturbed by cleaning. 255
House 1 House 2
1.00 1.00
0.75 0.75
0.50 0.50 Prevalence Prevelance
0.25 0.25
0.00 0.00
-12.5 -10.0 -7.5 -5.0 -2.5 -15 -10 -5 0 256 Log abundance Log abundance 257 258 Figure 6. Individual OTUs of fungi in the 4th week of sampling and their fit with the Sloan neutral model. 259
11 bioRxiv preprint doi: https://doi.org/10.1101/413773; this version posted September 11, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC 4.0 International license.
House 1
Antrodia Cladosporium_OTU12 Cladosporium_OTU8 Coprinellus Debaryomyces 20000 10000 20000 12000 15000 10000 7500 15000 9000 10000 10000 6000 5000 5000 5000 2500 5000 3000 0 0 0 0 0 Location 0 1 2 3 4 0 1 2 3 4 0 1 2 3 4 0 1 2 3 4 0 1 2 3 4 Back Didymella Filobasidium Malassezia Penicillium Rhodotorula Side 10000 5000 6000 15000 Abundance 7500 6000 4000 4000 10000 5000 4000 3000 2000 2000 5000 2500 2000 1000 0 0 0 0 0 0 1 2 3 4 0 1 2 3 4 0 1 2 3 4 0 1 2 3 4 0 1 2 3 4 Week House 2
Cladosporium Didymella Filobasidium Gloeosporium Malassezia 4e+05 750 1e+06 1000 400 500 3e+05 5e+05 2e+05 500 200 250 1e+05 0 0e+00 0e+00 0 0 Location 0 1 2 3 4 0 1 2 3 4 0 1 2 3 4 0 1 2 3 4 0 1 2 3 4 Back Malasseziales Nectriaceae Papiliotrema_OTU13 Papiliotrema_OTU373 Rhodotorula 800 800 6000 2500 800 Side Abundance 600 600 2000 600 4000 1500 400 400 400 2000 1000 200 200 500 200 0 0 0 0 0 0 1 2 3 4 0 1 2 3 4 0 1 2 3 4 0 1 2 3 4 0 1 2 3 4 260 Week 261 Figure 7. The change in abundance of the top most abundant fungal OTUs across the sampling period. 262 263 Conclusions 264 Given all these considerations, we would make the following recommendations as a 265 sampling scheme for longitudinal sampling of shower stalls. 266 • Inclusion of more than two houses is necessary to ensure a representative sample of 267 houses. 268 • At least 15 areas of the shower stall should be sampled at a given time point. These samples 269 will represent different samples in the larger metacommunity of that time point. 270 • The sampling area within the shower should be large vertical areas, extending as large as 271 possible to balance sufficient surface area to swab with enough area in the shower to allow 272 for undisturbed areas to swab in subsequent weeks. We anticipate that an area of 10cm x 273 100cm would yield sufficient biomass and allow room for repeated sampling over weeks. In 274 subsequent sampling periods, the sampling area could shift horizontally. 275 • Sampling should take place every two weeks and continue for at least ten weeks post 276 cleaning. 277 Despite the technological hurdles of implementation, these preliminary data indicate that, 278 even in areas of the household environment that are relatively favorable for microbial growth
12 bioRxiv preprint doi: https://doi.org/10.1101/413773; this version posted September 11, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC 4.0 International license.
279 compared to other (i.e. drier) places, immigrants arriving from human shedding dominate the 280 microbiota and that growth conditions are relatively still unfavorable for the time duration we 281 examined. It is unclear whether these taxa take up residence on bathroom showers or simply 282 persist on skin fragments and accumulate over time, and other tools, such as using isotopically- 283 labelled compounds (e.g. Berry et al. 2015), may be a more powerful approach to identifying 284 true ecological residents. These data also show that after a 4-week period, evidence for 285 deterministic processes is limited, indicating that periodic cleaning of at least this frequency is 286 sufficient to keep microbial communities associated with bathroom tiles in a non-steady state 287 of limited productivity. 288 289 Acknowledgements 290 We thank Annie Lai for help with sample processing, Dylan Smith for library preparation, and 291 Jon Palmer for assistance with running AMPtk. 292 293 References 294 Adams RI, Lymperopoulou DS, Misztal PK, Pessotti RDC, Behie SW, Tian Y, Goldstein AH, Lindow 295 SE, Nazaroff WW, and Taylor JW. 2017. Microbes and associated soluble and volatile 296 chemicals on periodically wet household surfaces. Microbiome 5:128. 297 Adams RI, Miletto M, Taylor JW, and Bruns TD. 2013. The diversity and distribution of fungi on 298 residential surfaces. PLoS ONE 8:e78866. 10.1371/journal.pone.0078866 299 Afanide B, Mabadeje S, and Naqvi S. 1976. Gibberella baccata, the perfect state of Fusarium 300 lateritium in Nigeria. Mycologia 68:1108-1111. 301 Angenent LT, Kelley ST, Amand AS, Pace NR, and Hernandez MT. 2005. Molecular identification 302 of potential pathogens in water and air of a hospital therapy pool. Proceedings of the 303 National Academy of Sciences of the United States of America 102:4860-4865. 304 Barberán A, Dunn RR, Reich BJ, Pacifici K, Laber EB, Menninger HL, Morton JM, Henley JB, Leff 305 JW, Miller SL, and Fierer N. 2015a. The ecology of microscopic life in household dust. 306 Proceedings of the Royal Society of London B: Biological Sciences 282. 307 Barberán A, Ladau J, Leff JW, Pollard KS, Menninger HL, Dunn RR, and Fierer N. 2015b. 308 Continental-scale distributions of dust-associated bacteria and fungi. Proceedings of the 309 National Academy of Sciences of the United States of America 112:5756-5761. 310 Berry D, Mader E, Lee TK, Woebken D, Wang Y, Zhu D, Palatinszky M, Schintlmeister A, Schmid 311 MC, and Hanson BT. 2015. Tracking heavy water (D2O) incorporation for identifying and 312 sorting active microbial cells. Proceedings of the National Academy of Sciences 313 112:E194-E203.
13 bioRxiv preprint doi: https://doi.org/10.1101/413773; this version posted September 11, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC 4.0 International license.
314 Burns AR, Stephens WZ, Stagaman K, Wong S, Rawls JF, Guillemin K, and Bohannan BJM. 2016. 315 Contribution of neutral processes to the assembly of gut microbial communities in the 316 zebrafish over host development. The ISME journal 10:655-664. 10.1038/ismej.2015.142 317 Byrd AL, Belkaid Y, and Segre JA. 2018. The human skin microbiome. Nature Reviews 318 Microbiology 16:143. 319 Callahan BJ, McMurdie PJ, Rosen MJ, Han AW, Johnson AJA, and Holmes SP. 2016. DADA2: high- 320 resolution sample inference from Illumina amplicon data. Nature Methods. 321 Davis NM, Proctor D, Holmes SP, Relman DA, and Callahan BJ. 2017. Simple statistical 322 identification and removal of contaminant sequences in marker-gene and 323 metagenomics data. bioRxiv:221499. 324 Dunn RR, Fierer N, Henley JB, Leff JW, and Menninger HL. 2013. Home Life: Factors structuring 325 the bacterial diversity found within and between homes. PLoS ONE 8:e64133. 326 10.1371/journal.pone.0064133 327 Edgar R. 2016. SINTAX: a simple non-Bayesian taxonomy classifier for 16S and ITS sequences. 328 bioRxiv:074161. 329 Edgar RC. 2010. Search and clustering orders of magnitude faster than BLAST. Bioinformatics 330 26:2460-2461. Doi 10.1093/Bioinformatics/Btq461 331 Falkinham JO, Pruden A, and Edwards M. 2015. Opportunistic premise plumbing pathogens: 332 increasingly important pathogens in drinking water. Pathogens 4:373-386. 333 Feazel LM, Baumgartner LK, Peterson KL, Frank DN, Harris JK, and Pace NR. 2009. Opportunistic 334 pathogens enriched in showerhead biofilms. Proceedings of the National Academy of 335 Sciences of the United States of America 106:16393-16398. 10.1073/pnas.0908446106 336 Fierer N, Lauber CL, Zhou N, McDonald D, Costello EK, and Knight R. 2010. Forensic 337 identification using skin bacterial communities. Proc Natl Acad Sci U S A 107. 338 10.1073/pnas.1000162107 339 Findley K, Oh J, Yang J, Conlan S, Deming C, Meyer JA, Schoenfeld D, Nomicos E, Park M, Becker 340 J, Benjamin B, Blakesley R, Bouffard G, Brooks S, Coleman H, Dekhtyar M, Gregory M, 341 Guan X, Gupta J, Han J, Hargrove A, Ho SL, Johnson T, Legaspi R, Lovett S, Maduro Q, 342 Masiello C, Maskeri B, McDowell J, Montemayor C, Mullikin J, Park M, Riebow N, 343 Schandler K, Schmidt B, Sison C, Stantripop M, Thomas J, Thomas P, Vemulapalli M, 344 Young A, Kong HH, and Segre JA. 2013. Topographic diversity of fungal and bacterial 345 communities in human skin. Nature 498:367–370. 10.1038/nature12171 346 Flores GE, Bates ST, Knights D, Lauber CL, Stombaugh J, and Knight R. 2011. Microbial 347 biogeography of public restroom surfaces. PLoS ONE 6. 10.1371/journal.pone.0028132 348 Gibbons SM, Schwartz T, Fouquier J, Mitchell M, Sangwan N, Gilbert JA, and Kelley ST. 2015. 349 Ecological Succession and Viability of Human-Associated Microbiota on Restroom 350 Surfaces. Applied and Environmental Microbiology 81:765-773. 10.1128/aem.03117-14 351 Grantham NS, Reich BJ, Pacifici K, Laber EB, Menninger HL, Henley JB, Barberán A, Leff JW, 352 Fierer N, and Dunn RR. 2015. Fungi identify the geographic origin of dust samples. PLoS 353 ONE 10:e0122605. 354 Koljalg U, Larsson KH, Abarenkov K, Nilsson RH, Alexander IJ, Eberhardt U, Erland S, Hoiland K, 355 Kjoller R, Larsson E, Pennanen T, Sen R, Taylor AFS, Tedersoo L, Vralstad T, and Ursing 356 BM. 2005. UNITE: a database providing web-based methods for the molecular
14 bioRxiv preprint doi: https://doi.org/10.1101/413773; this version posted September 11, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC 4.0 International license.
357 identification of ectomycorrhizal fungi. New Phytologist 166:1063-1068. Doi 358 10.1111/J.1469-8137.2005.01376.X 359 Lax S, Smith DP, Hampton-Marcell J, Owens SM, Handley KM, Scott NM, Gibbons SM, Larsen P, 360 Shogan BD, Weiss S, Metcalf JL, Ursell LK, Vázquez-Baeza Y, Van Treuren W, Hasan NA, 361 Gibson MK, Colwell R, Dantas G, Knight R, and Gilbert JA. 2014. Longitudinal analysis of 362 microbial interaction between humans and the indoor environment. Science 345:1048- 363 1052. 10.1126/science.1254529 364 Liu X-Z, Wang Q-M, Göker M, Groenewald M, Kachalkin A, Lumbsch HT, Millanes A, Wedin M, 365 Yurkov A, and Boekhout T. 2015. Towards an integrated phylogenetic classification of 366 the Tremellomycetes. Studies in Mycology 81:85-147. 367 McMurdie PJ, and Holmes S. 2013. phyloseq: an R package for reproducible interactive analysis 368 and graphics of microbiome census data. PLoS ONE 8:e61217. 369 Michielse CB, and Rep M. 2009. Pathogen profile update: Fusarium oxysporum. Molecular plant 370 pathology 10:311-324. 371 Miletto M, and Lindow SE. 2015. Relative and contextual contribution of different sources to 372 the composition and abundance of indoor air bacteria in residences. Microbiome 3:61. 373 10.1186/s40168-015-0128-z 374 Minich JJ, Zhu Q, Janssen S, Hendrickson R, Amir A, Vetter R, Hyde J, Doty MM, Stillwell K, and 375 Benardini J. 2018. KatharoSeq Enables High-Throughput Microbiome Analysis from Low- 376 Biomass Samples. mSystems 3:e00218-00217. 377 Moen B, Røssvoll E, Måge I, Møretrø T, and Langsrud S. 2016. Microbiota formed on attached 378 stainless steel coupons correlates with the natural biofilm of the sink surface in 379 domestic kitchens. Canadian Journal of Microbiology 62:148-160. 10.1139/cjm-2015- 380 0562 381 Palmer JM, Jusino MA, Banik MT, and Lindner DL. 2018. Non-biological synthetic spike-in 382 controls and the AMPtk software pipeline improve mycobiome data. PeerJ 6:e4925. 383 Perkins SD, Mayfield J, Fraser V, and Angenent LT. 2009. Potentially pathogenic bacteria in 384 shower water and air of a stem cell transplant unit. Applied and Environmental 385 Microbiology 75:5363-5372. 386 Pulliam HR. 1988. Sources, sinks, and population regulation. The American Naturalist 132:652- 387 661. 388 Quast C, Pruesse E, Yilmaz P, Gerken J, Schweer T, Yarza P, Peplies J, and Glöckner FO. 2013. 389 The SILVA ribosomal RNA gene database project: improved data processing and web- 390 based tools. Nucleic Acids Research 41:D590-596. 391 R Development Core Team. 2014. R: A Language and Environment for Statistical Computing. R 392 Foundation for Statistical Computing. version 3.3.2 ed. Vienna, Austria: http://www.R- 393 project.org. 394 Rognes T, Flouri T, Nichols B, Quince C, and Mahé F. 2016. VSEARCH: a versatile open source 395 tool for metagenomics. PeerJ 4:e2584. 396 Sloan WT, Lunn M, Woodcock S, Head IM, Nee S, and Curtis TP. 2006. Quantifying the roles of 397 immigration and chance in shaping prokaryote community structure. Environmental 398 Microbiology 8:732-740.
15 bioRxiv preprint doi: https://doi.org/10.1101/413773; this version posted September 11, 2018. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC 4.0 International license.
399 Zalar P, Novak M, De Hoog GS, and Gunde-Cimerman N. 2011. Dishwashers - A man-made 400 ecological niche accommodating human opportunistic fungal pathogens. Fungal Biology 401 115:997-1007. Doi 10.1016/J.Funbio.2011.04.007 402 Zupančič J, Novak Babič M, Zalar P, and Gunde-Cimerman N. 2016. The black yeast Exophiala 403 dermatitidis and other selected opportunistic human fungal pathogens spread from 404 dishwashers to kitchens. PLoS ONE 11:e0148166. 10.1371/journal.pone.0148166 405 Zymo Research. ZymoBIOMICS Microbial Community Standard. Available at 406 https://www.zymoresearch.com/zymobiomics-community-standard (accessed May 14 407 2018). 408 409 410 411
16