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Analyzing indoor mycobiomes through a large-scale citizen science study in Norway

Citation

Martin-Sanchez, Pedro M. et al. (2021), Analyzing indoor mycobiomes through a large-scale citizen science study in Norway, Dryad, Dataset, https://doi.org/10.5061/dryad.59zw3r24w

Abstract

In the built environment, fungi can cause important deterioration of building materials and adverse health effects for the occupants. Increased knowledge about indoor mycobiomes from different regions of the world, and their main environmental determinants, will enable improved indoor air quality management and identification of health risks. This is the first citizen science study about indoor mycobiomes at a large geographical scale in Europe, including 271 houses from Norway and 807 dust samples from three house compartments: outside of the building, living room and bathroom. The fungal community composition determined by DNA metabarcoding was clearly different between indoor and outdoor samples, but there were no significant differences between the two indoor compartments. The 32 selected variables, related to the outdoor environment, building features and occupant characteristics, accounted for 15% of the overall variation in community composition, with the house compartment as the key factor (7.6%). Next, the climate was the main driver of the dust mycobiomes (4.2%), while building and occupant variables had significant but minor influences (1.4% and 1.1%, respectively). The house-dust mycobiomes were dominated by ascomycetes (⁓70%) with Capnodiales and Eurotiales as the most abundant orders. Compared to the outdoor samples, the indoor mycobiomes showed higher species richness, which is probably due to the mixture of fungi from outdoor and indoor sources. The main indoor indicator fungi belonged to two ecological groups with allergenic potential: xerophilic molds and skin-associated yeasts. Our results suggest that citizen science is a successful approach for unraveling the built microbiome at large geographical scales.

Methods

ITS2 metabarcoding data using the primers gITS7 and ITS4.
A total of 863 samples in 9 libraries (96 unique barcodes). The quality-filtered dataset corresponds to 807 dust samples from 271 houses.

Sequencing was carried out at Fasteris SA (Plan-les-Ouates, Switzerland) using 3 full Illumina 250 bases paired-end MiSeq v3 runs (3 libraries per run).
 

Usage Notes

Raw sequences (fastq files) of this study are available on ENA at EMBL-EBI under accession number PRJEB42161 (https://www.ebi.ac.uk/ena/browser/view/PRJEB42161). Accession numbers for the nine libraries correspond to the BioSamples SAMEA7740226 - SAMEA7740234.

The HouseDustMyco dataset includes all revevant files and scripts to reproduce the bioinformatics and statistical analyses of this study, as well as the ReadMe file ("README_HouseDustMyco") that explains all contents of this dataset.

Funding

Horizon 2020, Award: Grant agreement No. 741332

H2020 Marie Skłodowska-Curie Actions, Award: Individual Fellowship to PMMS