ABSTRACT
Despite considerable efforts to characterize the ecology of bacteria and fungi in the built environment (BE), the metabolic mechanisms underpinning their colonization and successional dynamics remain unclear. Here, we applied bacterial/viral particle counting, qPCR, 16S and ITS rRNA amplicon sequencing, and metabolomics to longitudinally characterize the ecological dynamics of four commonly used building materials maintained at high humidity conditions (~94% RH). We varied the natural inoculum provided to each material by placing them in different occupied spaces, and we wet the surface of half of the samples of each material to simulate a flooding event. As expected, different materials showed different bacterial and viral particle abundance, with wet materials having higher growth rates and lower alpha diversity compared to non-wetted materials. Wetting described the majority of the variance in bacterial, fungal and metabolite structure, and material type only influenced bacterial and metabolic diversity, while location of inoculation was only weakly associated with bacterial and fungal beta diversity. Metabolites indicative of microbial activity were identified, as were those that were native to the surface material. Glucose-phosphate was abundant on all materials (except mold-free gypsum) and was correlated with Enterobacteriaceae, which could indicate a potential bacterial nutrient source. A compound consistent with scopoletin, a plant metabolite with antimicrobial activity, was significantly negatively correlated with Bacillus and positively correlated with Pseudomonas and enriched in medium density fiberboard (MDF) materials. In wet samples, the alkaloids nigragillin and fumigaclavine C, both with antimicrobial properties, were significantly positively correlated with the fungal phylum Ascomycota. Nigragillin, was also negatively correlated with Bacillus and Pseudomonas abundance. Thiabendazole and azoxystrobin (anti-fungal compounds) were highly abundant on mold-resistant gypsum wallboard and likely directly influenced the decreased fungal growth observed on this material. The mold-resistant gypsum material also showed a significant increase in bacterial alpha diversity, and bacterial and viral particle abundance, as well as a decrease in metabolite diversity, likely a result of reduced fungal growth. Penicillium taxa were positively correlated with thiabendazole, which suggested the persistence of resistant strains. Also, specific to the wet samples, Bacillus abundance was positively correlated with the azoxystrobin, suggesting bi-directional competitive adaptation, and positively correlated with metabolites known to interfere with Pseudomonas biofilm formation, which could explain the anti-correlation between these taxa. As expected, high moisture conditions enabled faster growth of inoculating microorganisms, whose composition, chemistry, and competition was shaped by surface material, suggesting that both fungal and bacterial growth need to be considered when determining the impact of dampness in built environments.
INTRODUCTION
The microbiology of the built environment comprises bacteria, archaea, fungi, viruses and protists, all of which maintain growth potentials under varying physicochemical regimes. Many recent studies of this ecosystem have applied molecular sequencing techniques to characterize microbial community relationships and dynamics under varying occupant density, building type and location, environmental conditions, and material type (Lax et al., 2017; Adams et al., 2016; Chase et al., 2016; Stephens, 2016; Lax et al., 2014). However, most of these studies have investigated communities sampled from relatively dry materials on which microbes are likely biologically inactive unless they experience liquid water or high relative humidity (RH) (Chase et al., 2016). It is widely accepted that fungal growth can occur at RH >75-80% and material decay can occur at RH >95%, depending on material (Viitanen et al., 2010, Johansson et al., 2012).
Dampness is a fairly common occurrence in buildings, with approximately half of all homes in the U.S. having experienced dampness or mold (IAQ Report - Prevalence of Building Dampness). Building material dampness occurs for different reasons, including: bulk liquid entry from floods, extreme weather events, and plumbing system problems; rain or snow entry through leaks in building envelopes and roofing systems; and high water vapor content resulting from moisture migration through building materials or condensation of warm humid air on cold surfaces (IAQ Report - Nature and Causes of Building Dampness). Dampness and the presence of visible mold have been consistently associated with adverse human health outcomes, including respiratory and allergic effects (Mendell et al., 2011, Quansah et al., 2012, Fisk et al., 2010, Jaakkola et al., 2013). Hypotheses to potentially explain these associations include a combination of exposure to specific microbial agents (Institute of Medicine, 2004), varied gene expression and metabolism (Hegarty et al., 2018), and the release of fungal metabolites including mycotoxins (Miller et al., 2014) and microbial volatile organic compounds (mVOCs) (Roze et al., 2013).
Although fungal growth on building materials has been studied for decades (Hyvärinen et al., 2002, Gravesen et al., 1999, Hoang et al., 2010; Pasanen et al., 1992), only a limited number of studies have used molecular techniques to investigate bacterial and fungal growth, microbial community dynamics, and/or metabolic activity on common buildings materials exposed to liquid water and/or high humidity conditions (Coombs et al., 2017). Therefore, we characterized the bacterial and fungal concentration and diversity, as well as the production of microbial metabolites, on samples of four common building materials incubated at ~94% relative humidity: oriented strand board (OSB), medium density fiberboard (MDF), gypsum wallboard, and mold-resistant gypsum wallboard. We varied the BE source of inoculation and purposely wet half of the samples to assess how indoor microbial sources and the presence of liquid water influence community structure and metabolite profiles of these materials over multiple time points. We used several techniques to quantify microbial growth and microbial community composition and functional metabolism including: bacterial and viral like particle counts, image processing of visible mold growth, qPCR, amplicon sequencing of 16S and ITS rRNA marker genes, and metabolomics. Results from these different methods were integrated via co-occurrence network approaches, which provided insights into microbial community organization and environmental interaction. Improved understanding of how bacterial and fungal metabolism is shaped by environmental properties (e.g., the presence of water, surface material composition) and inoculating source (e.g., building location, occupancy patterns) could have important implications for architectural design, construction, building management, and occupant health (Rand et al., 2017). Therefore, determining the microbial metabolic dynamics in these high RH environments should be an important research priority.
RESULTS
Experimental Setup
Our study used four building materials types: oriented strand board (OSB), medium density fiberboard (MDF), regular gypsum wallboard, and mold-resistant (i.e., mold-free, or ‘MF’) gypsum wallboard. Coupons of 5 cm × 5 cm of each material type were naturally inoculated at two different locations for about 50 days each. After the inoculation period concluded, the time that occupants were coming in close proximity was reported with similar values, a 0.16% and 0.18% of total time. The material coupons were sampled for off-line biological and chemical analysis at 7 different sampling time points, referred to here as TP0, TP1, TP2, TP3, TP4, TP5, and TP6. The initial samples (TP0) were taken just after retrieving the samples from the field inoculation and represent non-wetted, naturally inoculated samples previously held at normal residential humidity condition. After inoculation, half of each set of materials from each location were submerged in tap water in separate pans for ~12 hours to simulate the process of wetting of building materials due to a flood or leak. Different sampling strategies were tested and after statistical verification all samples of the same type were combined as technical replicates (Figure S1). Microbial datasets were later rarefied to same sequencing depth: 1,000 reads for bacteria and 10,000 reads for fungi. Unfortunately, rarefaction removed all bacterial samples from MDF materials, which had very low read counts. After rarefying the data, a comparison of the control and non-control samples reflected that control samples looked very similar in bacterial and fungi diversity than non-control samples, (mantel >= 0.49 and >= 0.43 for location 1 and location 2 respectively, all with a p < 1E-05), perhaps because air could still transmit through the non-hermetic foil cover and microbes from the interior of the wood (not killed with the sterilization) could have found their way to the surface. Highlighting that the coupon itself could still be an important contributor to microbial diversity for all samples. From these results, the control location was treated indistinctly than the other two locations. For more details see Materials and Methods section.
Visible growth, particulate counts and qPCR
Visible microbial growth occurred much faster and covered a far greater percentage of the surface area on wet tiles than on non-wetted tiles (Figure 1A). OSB and MDF had the greatest coverage and fastest growth: all wet OSB and MDF tiles reached at least 50% visible microbial coverage by day 20, while non-wetted tiles of these types reached < 25% coverage. No growth was ever visible on the mold-resistant gypsum tiles. Epifluorescence microscopy revealed that counts of bacterial like particles (BLP) and viral-like particles (VLPs) calculated on samples TP0 to TP3 were strongly correlated (R2 = 0.65, p = 2.8e-23) (Figure 1B), with VLP counts statistically lower than bacterial counts in all samples (ANOVA <=10-4) and in both wet and non-wet conditions (two sided non-parametric t-test p <=0.035) Figure 1C). This is in keeping with previous research that found very low VLP:bacteria ratios in the indoor environment (Prussin et al., 2015, Gibbons et al., 2015). In our dataset, the mean VLP-bacteria ratio was 0.86 ± 0.07, with a minimum of 0.61 and a maximum of 1.02 across all samples.
While BLP (only estimated for TP0 to TP3 samples) and bacterial qPCR agree that wetted samples had higher counts than non-wetted samples, cell counts inferred from these two methods drastically differ for different material types and over time. Most notably, the MF-gypsum had the greatest BLP counts but also the lowest 16S rRNA qPCR counts (6-fold or more lower than other materials). Moreover, the BLP cell counts were essentially constant over time, while qPCR counts steadily increased, with TP6 being 4-fold greater than TP1 and 209-fold greater than TP0 counts. To further confirm the differences, we calculated the overall correlation between paired bacterial qPCR and BLP counts and the results were not significant, emphasizing different biases for each method.
For fungal qPCR we observed MF-gypsum had the lowest abundance, while all other materials had a range of 20 to 118-fold increase over MF-gypsum. Wetted samples revealed a 4-fold increase in qPCR read abundance over non-wetted samples. Also, the qPCR read abundance increased steadily over time, in such a way that TP6 was 11-fold greater than TP1 and 750-fold greater than TP0 counts.
Bacterial, fungal and metabolite diversity
The bacterial and fungal communities in our study tended to decrease in diversity over time, as measured by the Shannon Index (Shannon H’), which incorporates both the richness and evenness of the community. Given that our data was rarified to an even depth before analysis, this decrease in diversity is indicative of the increasing relative abundance of certain community members, and suggests the preferential proliferation of certain taxa in the inoculating community. In our 16S dataset, wet samples experienced faster declines in diversity than non-wetted samples, and were significantly lower in diversity at the end of the study than non-wetted samples (Figure 2A), suggesting that certain bacterial taxa grew quickly in the wet environment and became dominant within the community. In our ITS dataset, we also observed a faster decline in diversity in wet samples, although wet samples were significantly more diverse than non-wetted samples by the end of the study (Figure 2B). The decrease in fungal diversity in wetted samples was not monotonic, with an initially steep decline and a subsequent increase. This may reflect fast growth by a small number of taxa that quickly dominated the community, followed by the growth of other taxa with slower growth rates. Similar patterns are observed when looking at the diversity changes for each individual material (Figure S2) with the exception of a lack of bacterial growth for wet MDF samples and reduced bacterial growth on dry OSB after the study was half way completed. In contrast, we observed no significant changes in the metabolic diversity over time for either wet or non-wetted samples (Figure 2C).
Microbial Compositional Changes
Across all samples, the diversity of bacteria within the community was significantly correlated to the diversity of fungi (Corr = 0.28, p=0.0003) (Figure 3A). Interestingly, neither bacterial nor fungal diversity was significantly correlated to the metabolite diversity, perhaps because of a narrower range of observed metabolite diversity compared to the taxonomic datasets. We observed striking changes in the relative abundance of certain bacterial (Figure 3B) and fungal (Figure 3C) genera over time, which were largely dependent on wetting condition. In the 16S rRNA dataset, Bacillus almost immediately came to dominate wet samples, with an average relative abundance as high as 50% after the 2nd time point, even though it represented a negligible part of the community at the start of sampling. Bacillus abundance also increased in non-wetted samples, although to a much smaller extent. A similar pattern was observed for the genera Pseudomonas and Erwinia, which also represented a very small fraction of community diversity at the start of sampling but quickly increased in abundance in wet (but not non-wetted) samples. Interestingly, a very large percentage of reads from early time point samples, both wet and non-wet, were of chloroplast origin. In wet samples, the number of chloroplast reads quickly declined as the bacterial genera proliferated. In non-wetted samples, chloroplast read abundance remained high, and dominated the sequencing effort to such an extent that discarding those reads would have dropped the majority of non-wetted samples below the rarefaction depth. While these likely represent residual DNA signatures from the plant material used to construct each tile material, we have chosen to keep them in the analysis. Figure S3A shows how this dynamic slightly vary for each different material type.
The majority of reads in the ITS dataset that could be taxonomically assigned to a genus belonged to one of two genera: Eurotium and Penicillium. Eurotium abundance was negligible at the beginning of community succession but quickly flourished in non-wetted samples, becoming the most abundant known genus in those samples by time point 2 (Figure 3C). By contrast, Eurotium did not become abundant across wet samples. Penicillium abundance was, on average, consistently higher in wet samples than in non-wetted samples, and its abundance was significantly anti-correlated to Eurotium relative abundance (corr = −0.12, p = 0.033). These taxa-specific changes were mirrored by community level differentiation, where wet vs. non-wetted tiles of the same material and inoculating location became significantly more dissimilar (Bray Curtis, Spearman’s Correlation, p<0.01) in both their bacterial and fungal community structure over time (Figure 3D). Figure S3B shows how this dynamic slightly vary for each different material type.
Environmental Factors Associate with Microbial and Metabolite Diversity
We used ANOSIM to calculate the factors significantly correlated with differences in the microbial communities across our three datasets. Bray-Curtis dissimilarity was calculated for the 16S, ITS, and metabolite datasets, and ANOSIM was used to determine whether distances between samples of the same metadata factor (i.e. wetting condition, inoculating location, and material) were significantly lower than distances between samples of different types (Figure S4). In our 16S rRNA dataset, wetting condition, location, and material each had a significant impact on bacterial community structure (all p <0.0001 based on 105 randomized permutations), with wetting having the most pronounced effect (R = 0.418). In general, non-wetted samples tended to be more similar to each other than wet samples were to each other, which is likely due to the dominance of a single chloroplast OTU. Material had a less pronounced effect (R=0.247) and location had the least evident effect on bacterial community structure (R=0.133).
Interestingly, fungal community structure was not significantly described by variance in material, while location had a relatively weak (R = 0.129) though highly significant (p < 0.0001) association, suggesting that variations in fungal communities that settle on materials (which have been shown to be driven largely by outdoor fungal communities, e.g., Adams et al., 2013) influence community structure upon experiencing wetting and high RH conditions. Wetting condition was by far the most influential factor influencing fungal community structure (R = 0.564, p < 0.0001), and in contrast to the bacterial data, wet samples were much more similar to each other than were non-wetted samples. Metabolite diversity within the community was also affected by wetting condition (R = 0.276, p < 0.0001), with non-wetted samples more similar to each other than wet samples. Material also played a significant role in metabolite diversity (R = 0.231, p < 0.0001), and mold-free gypsum samples were particularly metabolically similar, likely due to the lack of fungal growth and the underlying chemical composition of the material. Inoculating location had no significant effect on the diversity of metabolites despite having a significant effect on both the bacterial and fungal community membership. We visualized sample similarity using non-metric multi-dimensional scaling (NMDS) ordination based on Bray-Curtis dissimilarity (Figure 4). We converted material, location, and wetting condition into binary variables (1 = yes, 0 = no), which were fit onto the ordination, keeping only the significant vectors (R2 values for each vector and their significance is presented in Table S1). Visually, both bacterial and fungal beta diversity was more differentiated by wetting condition due to the significant increase in growth on wetted tiles, while metabolites were visually differentiated by both wetting condition and surface material, likely due to the underlying chemistry of the material and then the subsequent metabolic activity of the microbes when tiles were wetted.
Bacterial and fungal network co-occurrence
Using SparCC (Friedman & Alm, 2012), an algorithm developed to quantify correlations on microbial compositional data (data that has been subject to rarefaction), and a correlation threshold >0.4, uncovered co-occurrence patterns between taxa from each kingdom. In the bacterial network (Figure S5) three co-occurrence clusters were identified, the Bacillus cluster, Pseudomonas cluster, and a cluster comprising chloroplasts and mitochondria. As expected these groups correspond with the most abundant taxa. On all wet materials and on all samples of gypsum (both wet and non-wet), 95% of associations between Bacillus and Pseudomonas were negative correlations (Figure S6 and S7). On non-wetted OSB, MDF and MF-Gypsum there were no negative correlations between Pseudomonas and Bacillus. Interestingly, there is a dramatic increase in the absolute number of significant co-occurrence relationships between bacterial OTUs in wet (74) versus non-wetted samples (48), which is a 54% increase in the number of edges. In the fungal correlation network, Penicillium OTUs co-occurred with many unknown fungal genera, while OTUs corresponding to Aspergillus and its subset, Eurotium, maintained monophyletic clusters (Figure S8). As with the bacterial co-occurrence networks, fungal OTUs associated with wet tiles had negative correlations among each other, although the number was much smaller than for bacteria. Only 7 fungal OTUs were negatively correlated on wet materials, mainly between unknown genera and an abundant Penicillium OTU (Figure S9). Strikingly, unlike bacteria, the absolute number of significant co-occurrence relationships between fungal OTUs declined in wet (555) versus non-wetted samples (1,133), which was a 104% decrease in the number of edges, suggesting an inverse co-abundance response between bacteria and fungi during growth.
To better understand the co-associations between bacteria and fungi, 16S and ITS OTUs were co-correlated in a single network. A random walk-based method uncovered four distinct modules within the network, with a modularity of 0.45 (Figure 5A). In general, the taxa present in each sample tended to cluster within an individual network module (median sample association to module = 0.88). We correlated various metadata factors to module membership (Figure 5B) and observed that wetting condition had a significant impact on which samples dominated each module: modules 1 and 3 were associated with wet samples, while modules 2 and 4 were associated with non-wetted samples (Figure 5C). Location 1 samples dominated module 3, while Location 2 samples dominated module 1 (Figure 5D). Overall, wetting condition appears to be the most important factor driving community succession, resulting in two different community structures even when the source community is identical. We also visualized the nodes that were assigned to the genera previously discussed in Figure 3. Nodes in the bacterial genera Bacillus, Pseudomonas, and Erwinia, as well as the fungal genus Penicillium, were nearly exclusively enriched in the two wet-associated modules (1 and 3), while chloroplast reads and Eurotium nodes all clustered within the non-wetted modules (2 and 4; Figure 5E).
Metabolite network co-occurrence
A co-occurrence network correlation was calculated for the sample metabolite profiles (Figure 6). As these data are not compositional, we built this network using significantly positive Spearman correlations between nodes and included only the 1,000 most abundant metabolites in the dataset. This resulted in a network with 149,316 edges (density = 0.30) when the significance threshold (alpha) was set to 0.001. Using the same module discovery method described above, we uncovered 7 distinct modules (modularity = 0.32), excluding 12 metabolites around the periphery of the network that clustered into modules of <5 nodes. Three modules (3, 4, and 7) were significantly correlated with wet samples, while modules 1, 2, 5 and 6 were associated with non-wetted samples. There was almost no correlation between network modules and inoculating locations, further suggesting that while location (and hence the primary inoculating microbes) may influence community taxonomic diversity, it does not appear to strongly affect metabolic diversity during growth. The abundance of metabolites in module 7 were anti-correlated with all other modules, but specifically with module 2 (corr =-0.87, p<0.001). Module 7 is dominated by wet samples at later time points, suggesting that community succession in wet environments may converge to a common metabolic profile, which is wholly distinct from the non-wetted samples in module 2.
Metabolite features can predict sample type
Random Forest analysis was employed to determine the metabolites associated with various sample types. Models classifying whether a sample was wet had an average accuracy of 98% (error ratio = 25, with expected random error 0.5), and wet-samples were never misclassified as a non-wetted sample in any of the 10 model iterations. Models classifying samples based on material were similarly successful, with an average accuracy of 97% (error ratio = 25, with expected random error 0.75). Metabolomics models were much less successful at predicting the inoculating location, with a mean success of 72% (error ratio = 2.36 with expected random error 0.67). We sought to gain insight into the chemical composition of metabolites that comprise the signatures observed in these models. Feature importance scores were assigned to compounds based on their relative contributions to predicting sample type. For both the wetting condition dependent and material dependent groups, we selected the 100 highest-scoring metabolite features for further examination and chemical identification (Figure S10). Wet samples were enriched with 98 of the 100 top-scoring metabolites that differentiated wet and non-wetted. None of these compounds were automatically identified by mzCloud, so the metabolites were analyzed via external database searches, and compound classes were designated based on fragmentation spectra. A diverse set of compound families was observed, including compounds likely to be carbohydrates and glycoconjugates, fatty acids, prenol lipids, sterol lipids, polyketides, and glycerolipids, as well as several pyridine derivatives including a form of vitamin B6, indicative of microbial activity and growth and compounds associated with the surface materials.
Metabolites that were highly enriched in wet vs non-wetted conditions underwent additional manual analysis for confident structural identification. One of these metabolites was identified as Nigragillin (C13H22N2O, accurate mass = 222.1723), which is a fungal alkaloid first identified in Aspergillus niger (Isogai et al., 1975). Nigragillin abundance was significantly enriched in wet MDF and OSB samples (505- and 280-fold, respectively) compared to non-wetted samples. However, no significant differences in nigragillin were observed for gypsum or MF-gypsum. In both wet MDF and OSB the nigragillin concentration increases over time (Figure S11). Another high-scoring metabolite showed MS/MS fragmentation consistent with Fumigaclavine C (C23H30N2O2, accurate mass = 366.2291), which is a fungal alkaloid first identified in Aspergillus fumigatus (Cole et al., 1977). Fumigaclavine C was enriched in wet samples of gypsum, MDF, and OSB (23-, 26-, and 13-fold increase in comparison to non-wetted samples, respectively), with equivalent abundance in mold-free gypsum regardless of wetting. While the concentration of Fumigaclavine C remained flat or increased slowly in most materials, wet gypsum showed a dramatic increase in abundance at TP3 and 4 (Figure S11).
Metabolites that were predictive of material type (OSB, MDF, Gypsum and MF-Gypsum) were also further analyzed to determine how these materials influence the chemical composition of metabolites. Of these metabolites, 80% eluted with a retention time of >7 minutes, indicating a skew toward more hydrophobic compounds. This suggests that hydrophobic compounds are more diverse between the materials and therefore could have greater influence on microbial metabolism than the ubiquitous hydrophilic components. Two of these metabolites were identified by MzCloud search: glucose-phosphate, which was about 10-fold less abundant in MF-gypsum compared to all other materials, and scopoletin, a metabolite produced by the plants with antimicrobial activity Lerat et al., 2009, Gnonlonfin et al., 2012, Nascimento et al., 2013), which was about 60-fold more abundant in MDF samples than in other materials and could be influencing the reduced bacterial growth on this material (Figure S11). Thiabendazole and azoxystrobin, known anti-fungal compounds (Clausen & Yang, 2007, Balba, 2007), were highly overrepresented on MF-Gypsum, 333 and 595-fold respectively more abundant than the average content for the other three materials, and as such are likely some of the active compounds in MF-Gypsum.
Microbe-metabolite co-occurrences
The abundances of Nigragillin and Fumigaclavine C were each significantly positively correlated with a fungal OTU annotated to the phylum Ascomycota (corr = 0.66, FDR p = 0.0004), which contains species known to produce such alkaloids. Both Nigragillin and Fumigaclavine C have been reported to display antibacterial activity (Magdy et al., 2017 & Pinheiro et al., 2013). Interestingly, Nigragillin was negatively correlated with the abundance of Bacillus and Pseudomonas OTUs; this could suggest fungal competition for space and resources (Mille-Lindblom et al., 2006) against bacteria, and in the specific case of MDF, when Nigragillin abundance was greatest no bacterial growth was detected (Figure S2, Figure S11). The abundance of glucose-phosphate was significantly correlated to the proportion of a dominant Enterobacteriaceae OTU, a genus which is known to synthesize it (Herter et al., 2006) (corr = 0.72, FDR p = 0.000002). Thiabendazole was positively correlated with Penicillium abundance (corr = 0.80, FDR p < 10-9). As thiabendazole is prevalent and persistent in the natural environment, this correlation may indicate the presence of thiabendazole-resistant Penicillium strains colonizing the material from the built environment (Holmes & Eckert, 1999).
Co-occurrence networks were constructed between the bacterial OTUs and metabolites (SparCC correlation of >0.4; Figure S12) to explore further specific microbe-metabolite associations and possible mechanistic interactions. Significant correlations were observed between Bacillus OTUs and lipids including fatty acids and monoacylglycerophosphocholine compounds, which are likely to indicate cell wall and biofilm formation (Diomande et al., 2015, Dubois-Brissonnet et al., 2016). Interestingly, Bacillus OTUs were also positively correlated to other lipid classes including diols and flavonoids, which have all been observed to interfere with Pseudomonas biofilm formation (Kong et al., 2014; Jensen et al., 2014). In addition, the abundance of two fatty acids, one diol, and azoxystrobin, were positively correlated with Bacillus and negatively correlated with Pseudomonas. Conversely, scopoletin was positively correlated with Pseudomonas and negatively correlated with Bacillus (Figure S13). These additional antagonistic compound interactions between Bacillus and Pseudomonas could represent either competitive interactions between these organisms or different adaptation to the different materials and wetting conditions.
DISCUSSION
As expected, wetted materials had higher bacterial and fungal growth rates and were dominated by a few particular microbes, most notably the bacterial genera Bacillus, Erwinia, and Pseudomonas and the fungal genera Eurotium and Penicillium. This dominance led to an overall lower alpha diversity compared to non-wetted tiles. Wetting condition and material type described the majority of the variance in bacterial, fungal and metabolite structure. Interestingly, each wetted material showed its own unique microbe-metabolite dynamics.
Gypsum and MF gypsum were mostly colonized by Bacillus, with gypsum being a less selective environment, which allowed for several bacterial species to thrive on the same coupon simultaneously, each of them with high relative abundance and apparently sharing both the physical space and resources. In contrast, MF gypsum prevented most fungal growth and allowed Bacillus to dominate with little competition. MDF selected for fungal growth primarily, which allowed for the rapid accumulation of the antibacterial chemical, nigragillin, which is known to be made by the Aspergillus fungi. On OSB material, nigragillin and fumigaclavine C, a second fungal-synthesized antibacterial metabolite, may play important roles in microbial growth dynamics. Nigragillin, Fumigaclavine C, and Aspergillus relative abundance each gradually increases over time, whereas the abundance of Pseudomonas declines after the antibacterial metabolites reach peak abundance, suggesting a dose dependent response (Figure S11). These observations bolster our hypothesis that production of antibacterial metabolites by Aspergillus may inhibit the proliferation of surrounding bacteria. Also, there is a human health risk associated with the proliferation of the Aspergillus fungi in the BE. While the most common species identified in our data was Aspergillus penicillioides, a common indoor fungus in damp buildings with known associations to allergies and asthma (Edwards et al., 2012, Hay et al., 1992), other Aspergillus species are known to be able to produce mycotoxins (including aflatoxins), molecules that have been associated with cancer and immunosuppression on humans (Roze et al., 2013).
MOLD-RESISTANT GYPSUM
Traditional wood-based building materials contain natural polymers such as cellulose and lignin that are susceptible to degradation by fungal colonization (Gravesen et al., 1999, Pasanen et al., 1992). With some fungi having been shown to produce mycotoxins including aflatoxins that could affect human health (Roze et al., 2013, Rand et al., 2017), building materials such as mold-resistant gypsum have been developed, which contain antifungal compounds intended to discourage fungal growth. We were particularly interested to examine the microbial communities on these surfaces and as expected, found that fungal growth was diminished on MF-gypsum compared to other materials. However, it appeared that the scarcity of fungal colonies made way for bacterial species to flourish with less competition; on non-wetted materials we observed MF-gypsum bacterial particle counts greater than on the other three materials, and on wetted materials while the MF-gypsum bacterial counts were second to MDF, the abundance level between non-wetted and wetted tiles, unlike MDF, remained minimally changed. This raises the potential that pathogenic bacteria colonization could occur on MF-gypsum and if wetted could grow and lead to negative health outcomes. In terms of metabolite production, thiabendazole and azoxystrobin were some of the anti-fungal compounds found in high abundance and overall a similar subset of compounds accounted for most of the metabolite abundance on this material, indicating lower metabolic diversity when the colonization is dominated by bacterial growth. We also detected a correlation between thiabendazole and Penicillium, which suggested the persistence of thiabendazole-resistant fungal strains.
DIVERSITY AND INTERACTION BETWEEN MICROBES AND ENVIRONMENT
Additionally, certain lipid metabolites (indicative of biofilm formation) showed significant positive correlation with both Bacillus and Pseudomonas OTUs, and these lipids were negatively correlated with the abundance of chloroplast OTUs, indicating that when bacteria and metabolites indicative of biofilm formation are detected in greater abundance, we see a proportional reduction in plant-associated signal. Similar to the lipids, metabolites annotated to organic molecules and vitamins were also negatively correlated with the chloroplast OTUs, which suggests that bacterial growth, indicated by increased proportion of 16S, cellular counts and associated metabolites, tends to swamp out the background material-chloroplast signal. We hypothesize that this may be because these molecules are being produced by bacteria colonizing and forming biofilms on the woody material. When the relative abundance of the bacteria increases, it reduces our ability to detect chloroplast sequences (based on a given sequencing depth); as such this negative correlation is likely due to the increased abundance of the microbes that mediate the production of these metabolites, reducing the detection frequency of specific chloroplast OTUs, and not due to some mechanistic relationship between the wood and these molecules
MICROBIAL-METABOLITE INTERACTIONS
Pseudomonads and Bacillus are often the main contributors to biofilm formation on material surfaces in the built environment (Ronan et al., 2013; Powers et al., 2015; Raaijmakers et al., 2010). Biofilms are complex extracellular matrices formed by bacteria through the excretion of lipopeptide biosurfactants, to provide attachment to a surface to support colocalization with a nutrient source and protection from dehydration and chemical activity. Some of these lipopeptide biosurfactants produced by Pseudomonas and Bacillus species have been shown to have lytic or growth-inhibitory activity against many microorganisms such as bacteria, viruses, mycoplasmas, and fungi (Raaijmakers et al., 2010). Powers et al. demonstrated that Pseudomonas protegens produces antibiotics that inhibit biofilm formation and sporulation in Bacillus subtilis. They also found that Pseudomonas putida secretes an unknown inhibitory compound that prevented biofilm-associated gene expression. In our study we demonstrate a number of compounds known to have potential biofilm inhibitory qualities that also co-correlate with either Pseudomonas or Bacillus abundance, suggesting potential competitive activity between these organisms. While Pseudomonas–Bacillus interactions have been shown to be competitive, interspecies interactions within the genus Bacillus are also important in the formation of biofilms, lipids like hydroxy fatty acids and mono-acyl-glycerophosphocholines could be building blocks or residual products of the biofilm creation (Shank et al., 2011, Diomande et al., 2015, Dubois-Brissonnet et al., 2016).
CONCLUSION
The simultaneous collection of environmental, metabolomic and microbial profiles reveals insights into the chemical signals that may govern BE microbial communities under high humidity conditions, as well as providing evidence that these the membership compete for space and resources. Here we show that wetting condition can profoundly alter both fungal and bacterial community succession, and that the taxa which dominate samples after wetting or exposure to high humidity are not abundant in non-wetted materials and have little relation to the skin-associated taxa which dominate samples of indoor environments. After wetting, the microbial community undergoes a successional trajectory that can result in similar metabolic diversity even when taxonomic diversity remains variable. We further show that while material choice significantly influences bacterial diversity, the same is not true of fungal diversity. In summary, BE microbial ecology once seen as a wasteland (Gibbons, 2016) could rather be seen as a desert environment mostly formed with smaller assemblages that can rapidly become an active ecologically dynamic community if water, in liquid or vapor form, is added. When a material experience high moisture conditions, both fungal and bacterial growth rapidly accelerate and the metabolites associated with their adaptation to different surface materials and competition for resources demonstrate ready made eco-evolutionary adaptation to this sporadic availability of a crucial resource; this phenomenon is very similar to what has been observed in real desert soil microbiomes (Neilson et al., 2017), as well as in very different ecosystems, such as sediments exposed to oil pollution (Handley et al., 2017).
Methods
Test materials
Four building materials were used in this study: oriented strand board (OSB), medium density fiberboard (MDF), regular gypsum wallboard, and mold-resistant (i.e., mold-free, or ‘MF’) gypsum wallboard. All samples were purchased new from a home improvement store in Chicago, IL. The building materials were cut into 5 cm × 5 cm coupons for testing. The material coupons were sterilized by UV irradiation for 20 minutes followed by surface cleaning with a 70% ethanol solution.
Inoculation
The building material coupons were naturally inoculated by placing them uncovered on the floor inside two residences for a period of approximately 50 days each. The goal was to allow for natural settling of microbes from each indoor environment onto the material surfaces. Another set of test coupons was covered with aluminum foil and kept in the laboratory for the same duration to be used as a control group. Each set of test coupons included 44 coupons for each type of building material (i.e., 176 coupons in total) to allow for multiple subsequent sampling strategies. One set of test coupons was placed inside a 6th floor apartment unit with two adult occupants and a medium sized dog located in downtown Chicago, IL (Location 1). The other set of materials was placed inside a 2-story single-family residence without any pets near the main campus of Illinois Institute of Technology, approximately 8 km south of the downtown residence (Location 2). During the inoculation periods, built environment metadata (Ramos & Stephens 2014) were collected in each residence, including temperature (T) and relative humidity (RH) using Onset HOBO U12 data loggers and occupant presence within ~1 m range of the samples using Onset UX90 data loggers. Coupons at a third location (the Built Environment Research Laboratory at the of Illinois Institute of Technology) were covered with aluminum foil to minimize natural inoculation, serving as a control group.
Wetting and incubation
After inoculation, half of each set of materials (i.e., 22 coupons each) from each location, as well as 22 coupons from the control group, were submerged in sterilized tap water in separate pans for ~12 hours to simulate the process of wetting of building materials due to a flood or leak. The other half of each set of materials (i.e., the other 22 coupons each) from each location and the other 22 coupons from the control group were not submerged in water. Next, to encourage fungal growth on all of the building materials, all of the material coupons were placed in trays (each tray contained all 22 coupons of one type of material from one location or control group) and were incubated at room temperature (24 ± 2.7°C) inside a static airtight chamber (0.9 m × 1.2 m × 0.4 m). Salt solutions (potassium nitrate) were used to maintain high RH near ~94% for the duration of the experiment. Temperature and RH in the chamber were also recorded using Onset HOBO U12 data loggers.
Sampling procedures
The material coupons were sampled for off-line biological and chemical analysis at 7 different sampling time points, referred to here as: TP0, TP1, TP2, TP3, TP4, TP5, and TP6. The initial samples (TP0) were taken just after retrieving the samples from the field inoculation and represent non-wetted, naturally inoculated samples previously held at normal residential humidity conditions. The remaining sampling time points occurred every ~5 days. At each time point, a new coupon of each material from each condition that had never been swabbed before was swabbed, while duplicates of previously un-swabbed samples were also swabbed periodically (at TP0, TP2, TP4, and TP6) for comparison. Two samples (‘TP0’ and ‘TP0 duplicated’) were also swabbed at every time point to investigate whether repeatedly swabbing the surfaces impacted the results. Duplicates of both previously swabbed and previously un-swabbed samples were also included to investigate whether or not natural inoculation and subsequent growth was evenly distributed across multiple coupons. Figure S1 illustrates the experimental setup and Figure S14 shows coupons’ photographs at TP5 and TP6 for each one of the three locations. Details of swabbing procedure at each time point are described below.
First, sampling reagents were prepared as follows. Phosphate-buffered saline (PBS) was used for microbial samples that were to be analyzed for DNA and formaldehyde was used for microbial samples that were to be analyzed by microscopy. For PBS, 500 μl 1X PBS was added to 1.7 ml microtubes for each sample to be collected. For formaldehyde, 100 μl 4% paraformaldehyde was added to 1.7 ml microtubes for each sample. Microcentrifuge tubes were filled with ethanol solution (200 μl 50% EtOH) to preserve samples for surface chemistry/metabolomics analysis.
For subsequent DNA sequencing and analysis, the entire surface of the test coupons was swabbed using two BD Screw Cap SWUBETM Polyester swabs for approximately 20 sec. The same researcher swabbed every time to keep the swabbing process consistent. One of the double swabs was placed into the tube with PBS and frozen for shipping for subsequent sequencing. The tip of the other of the double swabs was placed into microtubes and the swab tips were vortexed for 10 seconds. 100 μL of sample buffer was removed added to the tube containing 100 μL 4% paraformaldehyde for fixation. These fixed samples were stored in a refrigerator at 4°C and then sent to the San Diego State University team for running numerical counts of cells and virus particles using microscopy.
For surface and microbial chemistry analysis (i.e., metabolomics), another test coupon was swabbed using a cotton-tipped applicator that is dipped in ethanol (Petras et al., 2016). The end of the swabs were cut directly into pre-prepared collection tubes, stored at 4 for 2-3 hours, and then stored at −20°C overnight. Swabs were then removed with clean forceps the next morning, then re-sealed into the microcentrifuge tubes and sent to the Northwestern University team on ice at −20 °C or lower. Overhead photos of each tray of coupons were also taken at each time point for image analysis using ImageJ to calculate the percentage of visible microbial growth coverage (Hoang et al. 2010).
Viral-like particle and bacterial microscopy counts
Epifluorescence microscopy was used to ensure that all samples contained bacteria and virus-like particles and to estimate their abundance. 100 μL of the paraformaldehyde-fixed samples were resuspended into 5 mL of sterile 0.02 μm filtered water. Each suspended sample was then filtered onto a 0.02 μm Whatman Anodisc filter membrane (Thurber et al., 2009). The filters were stained with 1X SYBR Gold and incubated for 10 minutes in the dark. Each filter was washed and mounted onto slides to be observed. Visualization was performed using a QImaging Retiga EXi Fast Cooled Mono 12-bit microscope and Image-Pro Plus software was used to collect digital images and estimate VLP and bacterial abundances.
Metabolomics analysis
Samples were analyzed by High-Performance Liquid Chromatography and High-Resolution Mass Spectrometry and Tandem Mass Spectrometry (HPLC-MS/MS). Specifically, the system consisted of a Thermo Q-Exactive in line with an electrospray source and an Agilent 1200 series HPLC stack including a binary pump, degasser, and autosampler, outfitted with a column (Waters XBridge BEH Shield RP18, 100×2.1 mm, 5μm particle size with matching guard). The mobile phase A was H2O with 0.1% Formic Acid; B was Acetonitrile with 0.1% Formic Acid. The gradient was as follows: 0-0.5 min, 98% A; 5 min, 80% A; 10-10.5 min, 5% A; 10.6-15 min, 98% A, with a flow rate of 400 μL/min. The capillary of the ESI source was set to 275 °C, with sheath gas at 40 arbitrary units and the spray voltage at 4.0 kV. In positive polarity mode, MS1 data was collected at a resolution of 35,000. The precursor ions were subsequently fragmented using the higher energy collisional dissociation (HCD) cell set to 30% normalized collision energy in MS2 at a resolution power of 17,500. Data were processed with Compound Discoverer 2.0 (Thermo Fisher) with MS/MS metabolite identifications made by comparing experimental MS/MS spectra with library spectra from MZCloud (lower cutoff score of 90% match).
For the metabolites that were selected for more in-depth characterization, classification of structure or substructure was performed by searching databases such as the Dictionary of Natural Products, the LIPID MAPS Structure Database, and GNPS (Global Natural Products Social Molecular Networking). Predicted structures resulting from a matched intact mass (≤10 ppm error) were subsequently validated through manual analysis of fragmentation mass spectra.
Metabolite differential abundances (fold calculations) were calculated from Compound Discoverer median peak areas for each compound including all three sampled locations.
DNA extraction and sequencing
To perform DNA extraction, the Qiagen DNeasy Powersoil HTP kit was used with a modified protocol optimized for low-biomass samples. Swab tips were inserted into each well of the bead plate, and then cut off using a sterilized wire cutter. The manufacturer’s protocol was then followed, with the following modifications: before cell lysis, the bead plates (containing beads, bead solution, swabs, and the C1 solution) were heated for 20 minutes at 60°C in a water bath. Additionally, the protocol steps using solutions C2 and C3 were combined into a single step, by adding 150 μl each of C2 and C3 together to the lysed sample in the 1 ml plate.
The DNA obtained from the DNA extraction was used for both high-throughput 16S/ITS sequencing, and qPCR. The 16S sequencing targeted the V4 region of the bacterial 16S rRNA gene, using the primer pairs 515F/806R. The ITS sequencing targeted the highly variable fungal internal transcribed spacer region located between the 5.8S and 18S rRNA genes. Both primer sets used the same reaction mix and thermocycler instructions: Reaction mix: 9.5 μL of molecular biology grade H2O, 12.5 μL of Accustart II PCR Toughmix, 1 μL each of forward and reverse primers at 5 μM, and 1 μL of sample DNA for a total reaction volume of 25 μL.
To make both the 16S and ITS amplicons, the following PCR program was used: Initial denaturing step at 94°C for 3 minutes, followed by 35 cycle of: 94°C for 45 seconds, 50°C for 60 seconds, and 72°C for 90 seconds, followed by a final extension step of 72°C for 10 minutes. The resulting amplicons were quantified using the Picogreen dsDNA binding fluorescent dye on a Tecan Infinity M200 Pro plate reader and pooled to 70 ng DNA per sample using the Eppendorf epMotion 5075 liquid handling robot. Primers and PCR reagents were removed using Agencourt AMPure beads, and then the clean amplicon pool was sequenced at Argonne National Laboratory’s Environmental Sample Preparation and Sequencing Facility, following the Earth Microbiome Protocol (Caporaso et al., 2011). Sequencing was performed on an Illumina Miseq using V3 chemistry, generating 2×150nt reads.
qPCR was performed using a Roche LightCycler 480 II. The 515F/806R primer pair was used again for amplification, using a mix of 10 μL Light Cycler 480 SYBR Green I Master mix, 6 μL of molecular biology grade H2O, 1 μL of 515F primer (10 μM), 1 μL of 806R primer (10 μM), and 2 μL of template DNA for a total of 20 μL per reaction. The following thermocycler conditions were used: (1) 95°C for 5 minutes, (2) 95°C for 10 seconds, (3) 45°C for 45 seconds, (4) Measure fluorescence, with steps 2 through 4 repeated 50 times. To determine the copy number of the 16S gene (and therefore the number of organisms per swab), a standard curve was generated using a serial dilution of a plasmid containing the E. coli 16S rRNA gene.
Treatment of Technical Replicates
We used Mantel test to determine whether the bacterial communities on replicate tiles (tiles on the same tray sampled at the same time) significantly resembled each other and preserved patterns of beta-diversity. We began by calculating the Bray-Curtis dissimilarity between each pair of samples taken from the same tile type using the beta_diversity.py function from the software QIIME 1.9.1 (Caporaso et al., 2010), producing dissimilarity matrices for each sampling type. Then the Mantel test and false discovery rate adjustment was performed using the mantel and p.adjust functions in the Vegan and stats R packages. For all comparisons between the sampling types the mantel statistic (which measures the stress in the fit of the two matrices) was significantly high (mantel >= 0.67 for fungi and >= 0.5 bacteria (all p < 1E-05) (Table S2 (Fungi) and S3 (Bacteria)). Based on the highly significant resemblance between tile types, we treated all samples of the same type as technical replicates, meaning that all combinations of material, location, wetting condition, and time point had either 2 (time point 0), 3 (time points 1, 3, and 5), or 4 (time points 2, 4, and 6) replicates.
Rarefaction and statistical analyses
After sequencing and sample merging, bacterial and Fungi OTU tables were rarefied to 1,000 and 10,000 reads respectively for statistical analyses. Rarefaction and statistical analyses were performed using R.
Random forest analyses
Random forest models were implemented using the “randomForest” R package. Samples from timepoint 0 were removed from the dataset. Models were built with 1000 trees and 10-fold cross validation. For each of the 10 models for each metadata criterion, a randomly drawn 70% of samples (100 samples) were used for model training and the remaining 30% (44 samples) were used for validation.
NMDS
We visualized sample similarity using non-metric multi-dimensional scaling (NMDS) ordination based on Bray-Curtis dissimilarity. Metadata vectors were fit onto the ordination using the envfit command in the Vegan R package. We converted material, location, and wetting condition into dummy variables (1 = yes, 0 = no) and, in the case of the bacterial and fungal datasets; also fit vectors of relative abundance for the common genera described in Figure 3. We assessed significance of each of the vectors using 105 permutations, and removed non-significant vectors from the figure. The R2 values for each vector and their significance is presented in Table S1
Co-occurrence networks
Traditional correlation networks are unsuited to genomic survey data as these data are relative, rather than absolute, measures of community composition. Since the relative abundances of all taxa within each sample must sum to 1, the fractions are not independent and will often exhibit negative correlations to each other regardless of the true correlation in absolute abundance. To avoid these compositional effects, we generated our networks using SparCC (Friedman & Alm, 2012), a correlation metric based on log-ratio transformed data that is specifically suited to compositional genomic surveys. Pseudo-p values for each correlation were generated through comparison from 100 to 1,000 bootstraps of the permuted OTU table.
For the same kingdom and microbe-metabolite networks only samples where either bacteria or fungi and metabolites were found in detectable levels after rarefaction were used (N=83, N= 91 respectively). Additionally, only bacterial OTUs with >9 reads, fungi OTUs with >99 reads, and metabolites with > 5’000,000 abundance in the rarified dataset were used for a total number of 630 bacterial OTUs, 352 fungi OTUs and 426 metabolites. Figures were generated using CAVNet R package (Cardona, 2017) and only displayed the higher correlation threshold (positive or negative) greater than 0.4.
For the network encompassing both bacteria and fungi, the OTUs reads threshold remained the same but only samples with both 16S and ITS data were kept (N = 153) producing a new subset of bacterial and fungi OTUs, 590 and 581 OTUs respectively. This dataset produced a co-occurrence network with 1,171 nodes. Only positive correlations with a pseudo-p < 0.05 were included, resulting in a network with 33,509 edges (density = 0.052). The network was ordinated using the Fruchterman-Reingold Algorithm (edge-weighted, force-directed) in the igraph R package, with node size based on the log read count of each OTU across all samples (with ITS counts first divided by 10 to equalize rarefaction depth between datasets). We used the Walktrap method (Pons & Latapy, 2005) to uncover dense subgraphs (modules) within the network, which may correspond to distinct community structures. We chose Walktrap (which is based on random walks within the network) as our method of community inference due to its computational tractability and its accuracy at uncovering subgraphs regardless of network size (Yang et al., 2016). We used random walks of four steps, which resulted in four distinct modules with a network modularity of 0.45