Longitudinal analysis of microbial interaction between humans and the indoor environment

Abstract

The bacteria that colonize humans and our built environments have the potential to influence our health. Microbial communities associated with seven families and their homes over 6 weeks were assessed, including three families that moved their home. Microbial communities differed substantially among homes, and the home microbiome was largely sourced from humans. The microbiota in each home were identifiable by family. Network analysis identified humans as the primary bacterial vector, and a Bayesian method significantly matched individuals to their dwellings. Draft genomes of potential human pathogens observed on a kitchen counter could be matched to the hands of occupants. After a house move, the microbial community in the new house rapidly converged on the microbial community of the occupants' former house, suggesting rapid colonization by the family's microbiota.

Figure 1. Differentiation in microbial community structure between homes and individuals

Density plots comparing the distributions of weighted UniFrac distances calculated within and between various criteria with accompanying ANOSIM tests of differentiation (all p values are less than 0.0001 based on 10,000 permutations of the randomized dataset). (A) Distribution of distances calculated between two human samples, between two home samples, and between a human sample and a home sample. An ANOSIM test on the effect of source produced a low Rvalue of 0.142, suggesting that home and human surfaces share a large degree of their microbial communities. (B) Distribution of distances for within home and between home comparisons of all samples taken from individual home surfaces. (C) Distribution of distances between human samples for the three sampled surfaces. Comparisons are segregated by whether a sample was compared to another from the same person, to a sample taken from an occupant of the same house, or to a sample taken from a resident of a different home. ANOSIM results are for tests on the effect of the home the sample was taken from (top) and of the individual the sample was taken from (bottom).

Figure 2. Widespread sharing of microbial taxa between human and home surfaces

(A) Plot of log₂-transformed average relative abundances in the human and home environments for all OTUs in the study with greater than 500 reads. OTUs are colored by whether their average relative abundance is significantly different between the home and person environments based on the FDR corrected p-value from a non-parametric t-test run with 1,000 permutations, and are sized based on their log₁₀-transformed number of reads. The dashed line is y=x, indicating an equal average relative abundance. (B) Fraction of all reads from within a source belonging to OTUs shared with other sources, demonstrating the ubiquitous sharing of OTUs between homes and the humans and pets that occupy them. The percent of reads that cluster within source-specific OTUs is less that 0.6% for all three sources. (C) Taxonomic summary of observed relative abundance of abundant phyla across all samples divided by source. (D) Taxonomic summary of observed relative abundance of taxa at class level for all reads in the study by source-specific OTU overlap. (E) Shared phylotypes heatmap for individual surfaces after consolidation of samples taken from the same surface type across temporal sampling series and homes. Pooled samples were rarified to an even depth of 277,500 reads.

Figure 3. Summary of predictive accuracy of Source Tracker models

(A) Percent composition estimate for the correct source for each home surface sample in the study. Samples within each block are ordered by collection date and black boxes occur where a sample is missing because it did not pass quality filtering standards. Across all surfaces, the models averaged a 59% prediction for the correct source. (B) Heatmap of model success across individual surface timeseries. The model was considered to be successful when the proportion of the sink community attributed to the correct source was greater than that attributed to any other source.