Dataset Information

Fungal and Bacterial Communities in Indoor Dust Follow Different Environmental Determinants.

ABSTRACT: People spend most of their time inside buildings and the indoor microbiome is a major part of our everyday environment. It affects humans' wellbeing and therefore its composition is important for use in inferring human health impacts. It is still not well understood how environmental conditions affect indoor microbial communities. Existing studies have mostly focussed on the local (e.g., building units) or continental scale and rarely on the regional scale, e.g. a specific metropolitan area. Therefore, we wanted to identify key environmental determinants for the house dust microbiome from an existing collection of spatially (area of Munich, Germany) and temporally (301 days) distributed samples and to determine changes in the community as a function of time. To that end, dust samples that had been collected once from the living room floors of 286 individual households, were profiled for fungal and bacterial community variation and diversity using microbial fingerprinting techniques. The profiles were tested for their association with occupant behaviour, building characteristics, outdoor pollution, vegetation, and urbanization. Our results showed that more environmental and particularly outdoor factors (vegetation, urbanization, airborne particulate matter) affected the community composition of indoor fungi than of bacteria. The passage of time affected fungi and, surprisingly, also strongly affected bacteria. We inferred that fungal communities in indoor dust changed semi-annually, whereas bacterial communities paralleled outdoor plant phenological periods. These differences in temporal dynamics cannot be fully explained and should be further investigated in future studies on indoor microbiomes.

SUBMITTER: Weikl F

PROVIDER: S-EPMC4839684 | biostudies-literature | 2016

REPOSITORIES: biostudies-literature

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Fungal and Bacterial Communities in Indoor Dust Follow Different Environmental Determinants.

Weikl Fabian F Tischer Christina C Probst Alexander J AJ Heinrich Joachim J Markevych Iana I Jochner Susanne S Pritsch Karin K

PloS one 20160421 4

People spend most of their time inside buildings and the indoor microbiome is a major part of our everyday environment. It affects humans' wellbeing and therefore its composition is important for use in inferring human health impacts. It is still not well understood how environmental conditions affect indoor microbial communities. Existing studies have mostly focussed on the local (e.g., building units) or continental scale and rarely on the regional scale, e.g. a specific metropolitan area. The ...[more]

PMID: 27100967

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Project description:The airborne transmission of COVID-19 has drawn immense attention to bioaerosols. The topic is highly relevant in the indoor hospital environment where vulnerable patients are treated and healthcare workers are exposed to various pathogenic and non-pathogenic microbes. Knowledge of the microbial communities in such settings will enable precautionary measures to prevent any hospital-mediated outbreak and better assess occupational exposure of the healthcare workers. This study presents a baseline of the bacterial and fungal population of two major hospitals in Kuwait dealing with COVID patients, and in a non-hospital setting through targeted amplicon sequencing. The predominant bacteria of bioaerosols were Variovorax (9.44%), Parvibaculum (8.27%), Pseudonocardia (8.04%), Taonella (5.74%), Arthrospira (4.58%), Comamonas (3.84%), Methylibium (3.13%), Sphingobium (4.46%), Zoogloea (2.20%), and Sphingopyxis (2.56%). ESKAPEE pathogens, such as Pseudomonas, Acinetobacter, Staphylococcus, Enterococcus, and Escherichia, were also found in lower abundances. The fungi were represented by Wilcoxinia rehmii (64.38%), Aspergillus ruber (9.11%), Penicillium desertorum (3.89%), Leptobacillium leptobactrum (3.20%), Humicola grisea (2.99%), Ganoderma sichuanense (1.42%), Malassezia restricta (0.74%), Heterophoma sylvatica (0.49%), Fusarium proliferatum (0.46%), and Saccharomyces cerevisiae (0.23%). Some common and unique operational taxonomic units (OTUs) of bacteria and fungi were also recorded at each site; this inter-site variability shows that exhaled air can be a source of this variation. The alpha-diversity indices suggested variance in species richness and abundance in hospitals than in non-hospital sites. The community structure of bacteria varied spatially (ANOSIM r 2 = 0.181-0.243; p < 0.05) between the hospital and non-hospital sites, whereas fungi were more or less homogenous. Key taxa specific to the hospitals were Defluvicoccales, fungi, Ganodermataceae, Heterophoma, and H. sylvatica compared to Actinobacteria, Leptobacillium, L. leptobacillium, and Cordycipitaceae at the non-hospital site (LefSe, FDR q ≤ 0.05). The hospital/non-hospital MD index > 1 indicated shifts in the microbial communities of indoor air in hospitals. These findings highlight the need for regular surveillance of indoor hospital environments to prevent future outbreaks.

Project description:Environmental microbes have been associated with both protective and adverse health effects in children and adults. Epidemiological studies often rely on broad biomarkers of microbial exposure (i.e. endotoxin, 1 → 3-beta-d-glucan), but fail to identify the taxonomic composition of the microbial community. Our aim was to characterize the bacterial and fungal microbiome in different types of environmental samples collected in studies of human health effects. We determined the composition of microbial communities present in home, school and outdoor air samples by amplifying and sequencing regions of rRNA genes from bacteria (16S) and fungi (18S and ITS). Samples for this pilot study included indoor settled dust (from both a Boston area birth cohort study on Home Allergens and Asthma (HAA) (n = 12) and a study of school exposures and asthma symptoms (SICAS) (n = 1)), as well as fine and coarse concentrated outdoor ambient particulate (CAP) samples (n = 9). Sequencing of amplified 16S, 18S, and ITS regions was performed on the Roche-454 Life Sciences Titanium pyrosequencing platform. Indoor dust samples were dominated by Gram-positive bacteria (Firmicutes and Actinobacteria); the most abundant bacterial genera were those related to human flora (Streptococcus, Staphylococcus, Corynebacterium and Lactobacillus). Outdoor CAPs were dominated by Gram-negative Proteobacteria from water and soil sources, in particular the genera Acidovorax, and Brevundimonas (which were present at very low levels or entirely absent in indoor dust). Phylum-level fungal distributions identified by 18S or ITS regions showed very similar findings: a predominance of Ascomycota in indoor dust and Basidiomycota in outdoor CAPs. ITS sequencing of fungal genera in indoor dust showed significant proportions of Aureobasidium and Leptosphaerulina along with some contribution from Cryptococcus, Epicoccum, Aspergillus and the human commensal Malassezia. ITS sequencing detected more than 70 fungal genera in indoor dust not observed by culture. Microbiome sequencing is feasible for different types of archived environmental samples (indoor dust, and low biomass air particulate samples), and offers the potential to study how whole communities of microbes (including unculturable taxa) influence human health.

Project description:Fungal-bacterial interactions play a key role in the functioning of many ecosystems. Thus, understanding their interactive dynamics is of central importance for gaining predictive knowledge on ecosystem functioning. However, it is challenging to disentangle the mechanisms behind species associations from observed co-occurrence patterns, and little is known about the directionality of such interactions. Here, we applied joint species distribution modeling to high-throughput sequencing data on co-occurring fungal and bacterial communities in deadwood to ask whether fungal and bacterial co-occurrences result from shared habitat use (i.e., deadwood's properties) or whether there are fungal-bacterial interactive associations after habitat characteristics are taken into account. Moreover, we tested the hypothesis that the interactions are mainly modulated through fungal communities influencing bacterial communities. For that, we quantified how much the predictive power of the joint species distribution models for bacterial and fungal community improved when accounting for the other community. Our results show that fungi and bacteria form tight association networks (i.e., some species pairs co-occur more frequently and other species pairs co-occur less frequently than expected by chance) in deadwood that include common (or opposite) responses to the environment as well as (potentially) biotic interactions. Additionally, we show that information about the fungal occurrences and abundances increased the power to predict the bacterial abundances substantially, whereas information about the bacterial occurrences and abundances increased the power to predict the fungal abundances much less. Our results suggest that fungal communities may mainly affect bacteria in deadwood.IMPORTANCE Understanding the interactive dynamics between fungal and bacterial communities is important to gain predictive knowledge on ecosystem functioning. However, little is known about the mechanisms behind fungal-bacterial associations and the directionality of species interactions. Applying joint species distribution modeling to high-throughput sequencing data on co-occurring fungal-bacterial communities in deadwood, we found evidence that nonrandom fungal-bacterial associations derive from shared habitat use as well as (potentially) biotic interactions. Importantly, the combination of cross-validations and conditional cross-validations helped us to answer the question about the directionality of the biotic interactions, providing evidence that suggests that fungal communities may mainly affect bacteria in deadwood. Our modeling approach may help gain insight into the directionality of interactions between different components of the microbiome in other environments.

Project description:BackgroundThe exposure of house occupants to indoor air pollutants has increased in recent decades. Among microbiological contaminants, bacterial and fungal aerosols remain poorly studied and the debate on the impact of these aerosols on respiratory health is still open. This study aimed to assess the diversity of indoor microbial communities in relationship with the health of occupants.MethodsMeasurements were taken from dwellings of 2 cohorts in Brittany (France), one with children without any pathology and the other with children and adults with asthma. Thirty dust samples were analyzed by next generation sequencing with a 16S and 18S targeted metagenomics approach. Analysis of sequencing data was performed using qiime 2, and univariate and multivariate statistical analysis using R software and phyloseq package.ResultsA total of 2,637 prokaryotic (589 at genus level) and 2,153 eukaryotic taxa were identified (856 fungal taxa (39%) and 573 metazoa (26%)). The four main bacterial phyla were identified: Proteobacteria (53%), Firmicutes (27%), Actinobacteria (11%), Bacteroidetes (8%). Among Fungi, only 136 taxa were identified at genus level. Three main fungal phyla were identified: Ascomycota (84%), Basidiomycota (12%) and Mucoromycota (3%). No bacterial nor fungal phyla were significantly associated with asthma versus control group. A significant over representation in control group versus asthma was observed for Christensenellaceae family (p-value = 0.0015, adj. p-value = 0.033). Besides, a trend for over representation in control group was observed with Dermabacteraceae family (p-value = 0.0002, adj. p-value = 0.815).ConclusionsOur findings provide evidence that dust samples harbor a high diversity of human-associated bacteria and fungi. Molecular methods such as next generation sequencing are reliable tools for identifying and tracking the bacterial and fungal diversity in dust samples, a less easy strategy for the detection of eukaryotes at least using18S metagenomics approach. This study showed that the detection of some bacteria might be associated to indoor air of asthmatic patients. Regarding fungi, a higher number of samples and sequencing with more depth could allow reaching significant signatures.

Dataset Information

Fungal and Bacterial Communities in Indoor Dust Follow Different Environmental Determinants.

Publications

Fungal and Bacterial Communities in Indoor Dust Follow Different Environmental Determinants.

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