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. 2018 Jun 1;126(6):067001.
doi: 10.1289/EHP3145. eCollection 2018 Jun.

Exposures Related to House Dust Microbiota in a U.S. Farming Population

Free PMC article

Exposures Related to House Dust Microbiota in a U.S. Farming Population

Mi Kyeong Lee et al. Environ Health Perspect. .
Free PMC article


Background: Environmental factors can influence the house dust microbiota, which may impact health outcomes. Little is known about how farming exposures impact the indoor microbiota.

Objective: We aimed to identify exposures related to bacterial communities in house dust in a U.S. farming population.

Methods: We used 16S rRNA amplicon sequencing to characterize bacterial communities in vacuumed dust samples from the bedrooms of a subset of 879 households of farmers and farmers' spouses enrolled in the Agricultural Lung Health Study (ALHS), a case-control study of asthma nested within the Agricultural Health Study (AHS) in North Carolina and Iowa. Information on current farming (past 12 mo), including both crop and animal farming, and other potential microbial sources was obtained via questionnaires. We used linear regression to evaluate associations between exposures and bacterial diversity within each sample, analysis of similarity (ANOSIM), and permutational multivariate analysis of variance (PERMANOVA) to identify exposures related to diversity between samples, and analysis of composition of microbiome to examine whether exposures related to diversity were also related to differential abundance of specific operational taxonomic units (OTUs).

Results: Current farming was positively associated with bacterial diversity in house dust, with or without adjustment for nonfarm exposures related to diversity, including presence of indoor pets, home condition, and season of dust collection. Many taxa exhibited differential abundance related to farming. Some taxa in the phyla Chloroflexi and Verrucomicrobia were associated [false discovery rate (FDR)<0.05] with farming but not with other nonfarm factors. Many taxa correlated with the concentration of house dust of endotoxin, commonly studied as a general marker of exposure to the farming environment.

Conclusions: In this farming population, house dust microbiota differed by current farming status. Understanding the determinants of the indoor microbiota is the first step toward understanding potential relationships with health outcomes.


Flow chart of the exposures related to house dust microbiota: the agricultural lung health study
Figure 1.
Workflow of our house dust microbiome study. This workflow includes a summary of sample selection from the Agricultural Lung Health Study (ALHS) (n=3,301) to the house dust microbiome study (n=879). It shows association analyses used in this paper: bacterial diversity analysis for both environmental exposures and endotoxin concentration, differential abundance analysis for environmental exposures, and differential relative abundance analysis for endotoxin.
Figure 2A is a pie chart showing all O T Us (n equals 1385): proteobacteria 27.1 percent, fermicutes 22.7 percent, actinobacteria 17.3 percent, bacteroidetes 17.1 percent, cyanobacteria 3.8 percent, chloroflexi 2.5 percent, unassigned 2.3 percent, acidobacteria 2 percent, fusobateria 1 percent, thermil 0.9 percent, verrucomicrobia 0.9 percent, TM7 0.8 percent, euryarchaeota 0.4 percent, tenericutes 0.3 percent, gemmatiomonadetes 0.3 percent, armatimonadetes 0.2 percent, FBP 0.2 percent, crenarchaeota 0.1 percent, and planctomycetes 0.1 percent. Figure 2B is a pie chart showing O T Us associated with indoor dog (n equals 94): firmicutes 34 percent, preteobacteria 25.5 percent, bacteroidetes 24.5 percent, actinobacteria 7.4 percent, fusobacteria 5.3 percent, thermil 1.1 percent, TM7 1.1 percent, and tenericutes 1.1 percent. Figure 2C is a pie chart showing O T Us associated with home condition (n equals 45): firmicutes 33.3 percent, proteobacteria 31.1 percent, bacteroidetes 13.3 percent, actinobacteria 11.1 percent, cyanobacteria 6.7 percent, and fusobacteria 4.4 percent. Figure 2D is a pie chart showing O T Us associated with winter (n equals 23): proteobacteria 34.8 percent, fermicutes 26.1 percent, cyanobacteria 17.4 percent, bacteroidetes 8.7 percent, unassigned 8.7 percent, and acidobacteria 4.3 percent. Figure 2E is a pie chart showing O T Us associated with crop farming (n equals 388): proteobacteria 25.3 percent, bacteroidetes 19.8 percent, firmicutes 19.8 percent, actinobacteria 18.8 percent, cyanobacteria 7.7 percent, chloroflexi 3.6 percent, acidobacteria 1.3 percent, unassigned 1.3 percent, TM7 0.8 percent, verrucomicrobia 0.5 percent, euryarchaeota 0.5 percent, FBP 0.3 percent, and armatimonadetes 0.3 percent. Figure 2F is a pie chart showing O T Us associated with animal farming (n equals 425): firmicutes 27.3 percent, proteobacteria 21.4 percent, bacteroidetes 21.2 percent, actinobacteria 17.9 percent, chloroflexi 4.2 percent, cyanobacteria 2.8 percent, acidobacteria 1.4 percent, unassigned 1.4 percent, verrucomicrobia 0.7 percent, TM7 0.7 percent, euryarchaeota 0.5 percent, FBP 0.2 percent, and thermil 0.2 percent. Figure 2G is a pie chart showing O T Us associated with both crop and animal farming (n equals 631): proteobacteria 25.2 percent, firmicutes 23.1 percent, actinobacteria 19.3 percent, bacteroidetes 18.1 percent, cyanobacteria 4.8 percent, chloroflexi 3.3 percent, acidobacteria 1.7 percent, unassigned 1.4 percent, verrucomicrobia 0.6 percent, TM7 0.6 percent, euryarchaeota 0.5 percent, thermil 0.5 percent, FBP 0.3 percent, crenarchaeota 0.2 percent, planctomycetes 0.2 percent, and armatimonadetes 0.2 percent.
Figure 2.
Proportion of operational taxonomic units (OTUs) significantly associated [falsediscoveryrate(FDR)<0.05] with each exposure at the phylum level. Pie chart shows proportions of significant OTUs assigned to each phylum in relation to each exposure. Phyla having more than one OTUs were shown in the pie chart. Of all OTUs (n=1,385) in our data, there were (A) 1,353 assigned to 18 phyla, 32 OTUs unassigned. Of the 1,385 OTUs, (B) 94 within 8 phyla were significantly associated with presence of an indoor dog (vs. no indoor dog); (C) 45 within 6 phyla were associated with home condition (higher vs. lower); (D) 21 within 5 phyla and 2 unassigned were associated with winter (vs. other seasons combined); (E) 383 within 12 phyla and 5 unassigned were associated with crop farming (vs. no crop farming); (F) 419 within 12 phyla and 6 unassigned were associated with animal farming (vs. no animal farming); and (G) 622 within 15 phyla and 9 unassigned were associated with both crop and animal farming (four-level combined variable) adjusted for presence of an indoor dog (vs. no indoor dog), home condition (higher vs. lower), and winter (vs. other seasons combined).

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