Identification of Household Bacterial Community and Analysis of Species Shared with Human Microbiome
- 4.3k Downloads
Microbial populations in indoor environments, where we live and eat, are important for public health. Various bacterial species reside in the kitchen, and refrigerators, the major means of food storage within kitchens, can be a direct source of food borne illness. Therefore, the monitoring of microbiota in the refrigerator is important for food safety. We investigated and compared bacterial communities that reside in the vegetable compartment of the refrigerator and on the seat of the toilet, which is recognized as highly colonized by microorganisms, in ten houses using high-throughput sequencing. Proteobacteria, Firmicutes, Actinobacteria, and Bacteroidetes were predominant in refrigerator and toilet samples. However, Proteobacteria was more abundant in the refrigerator, and Firmicutes was more abundant in the toilet. These household bacterial communities were compared with those of human skin and gut to identify potential sources of household bacteria. Bacterial communities from refrigerators and toilets shared more species in common with human skin than gut. Opportunistic pathogens, including Propionibacterium acnes, Bacteroides vulgatus, and Staphylococcus epidermidis, were identified as species shared with human skin and gut microbiota. This approach can provide a general background of the household microbiota and a potential method of source-tracking for public health purposes.
KeywordsBacterial Community Human Skin Indoor Environment Firmicutes Swab Sample
Indoor microbes have been studied in the context of human health using culture-dependent and -independent techniques. Most studies focused on the bacterial contamination of surfaces in kitchens and restrooms, which are easily colonized by microbes [9, 10, 15, 22, 24]. Some pathogenic bacteria can survive on the surfaces in these environments for some time, and contamination of food by these pathogenic bacteria can cause illness. Microbial contaminations of refrigerators have been studied, because refrigerators are used to store food [2, 4, 7, 14]. Moisture and nutrients (food particles) in refrigerators provide favorable growth conditions for contaminating bacteria from unwashed raw foods, leaking packages, and hands. In particular, higher bacterial counts and temperatures in vegetable compartments could cause critical problems . Recently, a German outbreak caused by Shiga-toxin producing Escherichia coli O104:H4 illustrated that unwashed vegetables could be a risk element . Therefore, the study of bacterial contamination in the vegetable compartments of refrigerators is important for public health.
Most of the previously reported culture-dependent studies of kitchen and refrigerator microbes focused on pathogen detection [7, 14, 21, 22, 30]. The recent advent of next generation sequencing techniques provides unprecedented data on the microbial composition, and the ecology of various environments, including indoor spaces [9, 10, 12, 15]. Analyses of microbes in various environments by high-throughput sequencing can benefit various fields, including source-tracking. Identification of the sources of bacterial contamination in indoor environment is important for managing food safety. Human skin is a primary source of bacteria in indoor environments, and individuals can transmit bacterial pathogens by touching indoor spaces [9, 10]. Comparing various parts of the human microbiome with microbial communities in indoor environments can identify bacterial species commonly found in both environments and thereby suggest the source of contamination or transmission.
In this study, we characterized bacterial communities within vegetable compartments of refrigerators and on toilet seats by using pyrosequencing based on 16S rRNA genes. The comparison of bacterial communities analyzed in this study with published human microbiome data provides further insight into shared species and sources of bacteria on the surfaces of refrigerators and toilets. Opportunistic pathogens were shared between the human skin microbiome and microbial populations in refrigerators and toilets.
Materials and Methods
Sampling and DNA Extraction
Swab samples were obtained from 5 × 5 cm surfaces of refrigerators (vegetable compartments) and toilets (seat part) at ten houses using an Easy swab kit (KOMED, Korea). Sampling was carried out in households with 4−5 family members. Samples were transported back to the laboratory under chilled conditions (4 °C) and processed within 6 h. To analyze culturable and unculturable bacterial communities, the genomic DNA on swab samples was extracted by two different methods. For culturable bacterial community, diluted swab samples (10−2) were inoculated on plate count agar (PCA; BD-Difco, Sparks, MD, USA) and nutrient agar (NA; BD-Difco) and incubated for 48 h at 30 °C. The surface of the cultured agar medium was washed and suspended in 1 mL of the extraction buffer from a FastDNA SPIN extraction kit (MP Biomedicals, Santa Ana, CA, USA) using a disposable spreader (SPL Life Sciences, Korea). Genomic DNA from the washed plates was then extracted using a FastDNA SPIN extraction kit. For unculturable bacterial community, metagenomic DNA in swab samples from refrigerators and toilets was extracted using a FastDNA SPIN extraction kit by following the manufacturer’s instructions.
16S rRNA gene fragments corresponding to the V1−V3 regions were amplified from the genomic DNA of culture washing solutions and swab metagenomic samples using a previously described method . For PCR, amplifications were performed in a final volume of 50 μL containing 10× Taq buffer, dNTP mixture (Takara, Shiga, Japan), 10 μM of each barcoded fusion primer (http://oklbb.ezbiocloud.net/content/1001), and 2 U of Taq polymerase (ExTaq, Takara) by a C1000 Touch thermal cycler (Bio-Rad, Hercules, CA, USA). After initial denaturation at 94 °C for 5 min, the product was amplified by 30 cycles of denaturation (30 s, 94 °C), primer annealing (30 s, 55 °C), and extension (30 s, 72 °C), with a final extension step of 7 min at 72 °C. The PCR product was confirmed by 2 % agarose gel electrophoresis and visualized under a Gel Doc system (Bio-Rad). The amplified products were purified with a QIAquick PCR purification kit (Qiagen, Valencia, CA, USA) and quantified using a PicoGreen dsDNA Assay kit (Invitrogen, Carlsbad, CA, USA). Equimolar concentrations of each amplicon from different samples were pooled and purified using an AMPure bead kit (Agencourt Bioscience, Beverly, MA, USA) and then amplified on sequencing beads by emulsion PCR. Recovered beads from emulsion PCR were deposited on a 454 Picotiter Plate and sequenced with a Roche/454 GS Junior system by following the manufacturer’s instructions.
Raw sequence files were processed by (1) demultiplexing, (2) trimming primer sequence, (3) quality filtering, (4) sequencing error correction, (5) taxonomic assignment, and (6) detection of chimeras. Each sample was identified by a unique barcode in the demultiplexing step and low quality reads (average quality score <25 or read length <300 bp) were removed for further analysis. Pairwise sequence alignment and the hmm-search program of the HMMER 3.0 package  were used to trim primer sequences based on the profile of the 16S rRNA V1−V3 regions. To correct sequencing errors, representative sequences in clusters of trimmed sequences were chosen and considered for taxonomy identification (details in Supplementary Methods). Individual reads were assigned their taxonomic positions according to the highest pairwise similarity among the top five BLASTN hits against the EzTaxon-e database . Chimera sequences were removed by UCHIME . The read number in each sample was normalized by random subsampling. The diversity indices and species richness were calculated using three different methods: Cluster Database at High Identity with Tolerance (CD-HIT), Taxonomy-Based Clustering (TBC), and Taxonomy-Dependent Clustering (TDC)-TBC (details in Supplementary Methods). The compositions and proportions of bacterial species shared between two samples or sets of multiple samples were calculated using CLcommunity software (ChunLab, Inc., Korea). Similarity coefficients of Bray-Curtis, Jaccard and Sorenson abundance were calculated using Mothur , and the matrix of Fast UniFrac  was generated using CLcommunity. Principal coordinate analyses (PCoA) were used to represent the relationships between samples using calculated similarity coefficients. The significance of difference among bacterial communities was calculated by Libshuff analysis using Mothur. Pyrosequencing reads generated in this study are available at the EMBL SRA database under the study accession number ERP002164 (http://www.ebi.ac.uk/ena/data/view/ERP002164).
Results and Discussion
Comparison of Bacterial Communities Originated from Surfaces of Refrigerators and Toilets
The compositions of the top ten most prevalent genera in each sample showed clear differences between bacterial communities of refrigerators and toilets (Fig. 1b). Pseudomonas and Pantoea within Gammaproteobacteria were identified as the dominant genera in refrigerator samples. Although the genus Pseudomonas was also dominant in toilet samples, the proportion of Pantoea was relatively low and Bacillus, Staphylococcus, and Streptococcus within Firmicutes were dominant genera. The bacterial communities present in the individual samples obtained from each house are presented in Supplementary Fig. S1. The number of toilet samples was smaller than that of refrigerator samples because, sufficient DNA was not always isolated from swab samples of toilet seat surfaces. This is probably because toilet surfaces are cleaned more frequently than the vegetable compartments of refrigerators in general households. The compositions of bacterial communities in refrigerators of most houses obtained by plate washing method were similar to those obtained by culture-independent methods, except #6 house. However, only 5 out of 30 phyla were detected in the plate washing results, and the proportions of each member in bacterial communities were different between two methods. The differences between culture-based plate washing and culture-independent surveys were significant in toilet samples obtained from identical houses (#1 and 3): Firmicutes and Actinobacteria were more abundant in culture-based plate washing results. This difference could be due to the selectivity of PCA or NA media for cultured bacteria found in the bacterial community on toilet seats. The genus Staphylococcus was the most dominant bacteria obtained by culture-based plate washing method in toilet samples (average 45.9 % of total reads). The phylum and genus compositions in the refrigerator and toilet samples were unique because the people and their behaviors (e.g., frequency of cleaning, cleaning products used, kinds of refrigerators and toilets, and usage patterns) varied in each household.
Identification of Bacterial Species Shared with Human Microbiome
Comparison of PCoA Plots Based on Four Different Statistical Calculations of Community Distance
The initiation of food borne illness has been reported to occur more frequently in private homes than in commercial operations [28, 29]. Refrigerators in kitchens could be colonized by bacteria, and these bacteria might contaminate other stored foods or attach to and survive on the internal surface of the refrigerator, thereby posing risks of indirect, long-term contamination during subsequent food preparation activities [20, 21, 22, 30]. In this study, most bacteria detected were probably not pathogens or opportunistic pathogens, and genera belonging to common pathogens were detected in only a very small fraction of communities on the surfaces of refrigerators and toilets. However, their presence could influence other microorganisms, since they survive on and are transmitted to the surfaces of indoor environments. This potential risk can be prevented by wrapping stored foods and regularly cleaning indoor environments, including refrigerators. The expansion of studies on indoor microbial communities using high-throughput molecular methods will advance our understanding of microorganisms in indoor environments and improve preventive measures for public health.
This study was supported by Industrial Strategic Technology Development Program (10040176) funded by the Korean Ministry of Knowledge Economy (MKE).
- 18.Marinelli LJ, Fitz-Gibbon S, Hayes C, Bowman C, Inkeles M, Loncaric A, Russell DA, Jacobs-Sera D, Cokus S, Pellegrini M et al (2012) Propionibacterium acnes bacteriophages display limited genetic diversity and broad killing activity against bacterial skin isolates. mBio 3(5):00279. doi: 10.1128/mBio.00279-00212 CrossRefGoogle Scholar
- 27.Schloss PD, Westcott SL, Ryabin T, Hall JR, Hartmann M, Hollister EB, Lesniewski RA, Oakley BB, Parks DH, Robinson CJ et al (2009) Introducing mothur: open-source, platform-independent, community-supported software for describing and comparing microbial communities. Appl Environ Microbiol 75(23):7537–7541PubMedCrossRefGoogle Scholar
Open AccessThis article is distributed under the terms of the Creative Commons Attribution License which permits any use, distribution, and reproduction in any medium, provided the original author(s) and the source are credited.