The Diversity and Distribution of Fungi on Residential Surfaces

<div><p>The predominant hypothesis regarding the composition of microbial assemblages in indoor environments is that fungal assemblages are structured by outdoor air with a moderate contribution by surface growth, whereas indoor bacterial assemblages represent a mixture of bacteria entered from outdoor air, shed by building inhabitants, and grown on surfaces. To test the fungal aspect of this hypothesis, we sampled fungi from three surface types likely to support growth and therefore possible contributors of fungi to indoor air: drains in kitchens and bathrooms, sills beneath condensation-prone windows, and skin of human inhabitants. Sampling was done in replicated units of a university-housing complex without reported mold problems, and sequences were analyzed using both QIIME and the new UPARSE approach to OTU-binning, to the same result. Surfaces demonstrated a mycological profile similar to that of outdoor air from the same locality, and assemblages clustered by surface type. “Weedy” genera typical of indoor air, such as <i>Cladosporium</i> and <i>Cryptococcus,</i> were abundant on sills, as were a diverse set of fungi of likely outdoor origin. Drains supported more depauperate assemblages than the other surfaces and contained thermotolerant genera such as <i>Exophiala</i>, <i>Candida</i>, and <i>Fusarium</i>. Most surprising was the composition detected on residents’ foreheads. In addition to harboring <i>Malassezia</i>, a known human commensal, skin also possessed a surprising richness of non-resident fungi, including plant pathogens such as ergot (<i>Claviceps purperea</i>). Overall, fungal richness across indoor surfaces was high, but based on known autecologies, most of these fungi were unlikely to be growing on surfaces. We conclude that while some endogenous fungal growth on typical household surfaces does occur, particularly on drains and skin, all residential surfaces appear – to varying degrees – to be passive collectors of airborne fungi of putative outdoor origin, a view of the origins of the indoor microbiome quite different from bacteria.</p></div

Predictive factors of fungal community composition on surfaces within residential apartments.

<p>Notes: Community composition based on the Morisita-Horn index, with values are reported after factoring out sequencing plate effects. ¹For all samples, type refers to drain, windowsill, or skin swab. For drains, type refers to bathroom sink, bathtub drain, or kitchen sink. ²Room function not included in model for skin only samples. ns  =  not significant. df  =  degrees of freedom.</p

Visual representation of the fungal composition on different surface types in residences using nonmetric multidimensional scaling (NMDS) based on the Morisita-Horn (abundance-based) index.

<p>The composition of fungi cluster by surface type, with drains showing higher variation across samples than skin and sills (NMDS stress = 0.13).</p

Classes of Fungi Represented across Different Parts of the Built Environment.

<p>Panel A: Eighteen different classes of fungi were represented on sills, drains, and skin. Both the broad composition of fungal taxa as well as their relative abundances are similar across the different surfaces, as well as to passively-settled dust from the outdoor air at the same location <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0078866#pone.0078866-Adams1" target="_blank">[10]</a>. Particularly abundant are Agaricomycetes (mushrooms, broadly), Dothidiomyetes (including molds and plant pathogens), Eurotiomycetes (yeasts), Sordariomycetes (including molds), and Tremellomycetes (plant and soil-associated). Panel B: Drains stand distinct from other surfaces and dust samples when considering only those frequent taxa present in at least 10% of each sample type. Drains appear less rich and are relatively overrepresented by Eurotiomycetes (<i>Exophiala</i>), Microbotryomycetes (<i>Rhodotorula</i>), and Sordariomycetes (<i>Fusarium</i>).</p

Identify of common fungi and their frequency across samples, as percentages.

<p>Notes: ¹ Match to sequences in the UNITE fungal database <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0078866#pone.0078866-Abarenkov1" target="_blank">[28]</a>, version released August 26, 2013.</p

Detection of toxic metabolites in raw and ripened Pu-erh samples.

<p>Raw Pu-erh samples are indicated by circles, and ripened samples by squares. Mean concentrations and standard deviations of each metabolite in raw (in blue) and ripened (in orange) Pu-erh samples are marked.</p

Visualization of differences in bacterial (A) and fungal (B) community composition based on binary Bray-Curtis index.

<p>Principal coordinates plot showing relationship among samples, where summer samples are circles, winter samples are squares, and room locations are color-coded.</p

The first 15 most abundant fungal OTUs in fresh leaf, raw and ripened Pu-erh samples.

<p>The first 15 most abundant fungal OTUs in fresh leaf, raw and ripened Pu-erh samples.</p

PCoA of Binary-Jaccard dissimilarities of microbial communities of fresh tea leaf (red), raw (blue) and ripened (orange) Pu-erh samples.

<p>The oldest raw Pu-erh sample (A6, 28 years old), indicated by an arrow in both PCoA analyses, is more similar to ripened Pu-erh than to other raw Pu-erh samples.</p

Comparison among fresh leaf, raw Pu-erh and ripened Pu-erh samples at β-diversity level.

<p>Comparison among fresh leaf, raw Pu-erh and ripened Pu-erh samples at β-diversity level.</p