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. 2013 Aug 12;8(8):e70538.
doi: 10.1371/journal.pone.0070538. eCollection 2013.

Characterization of Staphylococcus and Corynebacterium clusters in the human axillary region

Chris Callewaert  1 , Frederiek-Maarten Kerckhof, Michael S Granitsiotis, Mireille Van Gele, Tom Van de Wiele, Nico Boon
Affiliations

Characterization of Staphylococcus and Corynebacterium clusters in the human axillary region

Chris Callewaert et al. PLoS One. .

Abstract

The skin microbial community is regarded as essential for human health and well-being, but likewise plays an important role in the formation of body odor in, for instance, the axillae. Few molecular-based research was done on the axillary microbiome. This study typified the axillary microbiome of a group of 53 healthy subjects. A profound view was obtained of the interpersonal, intrapersonal and temporal diversity of the human axillary microbiota. Denaturing gradient gel electrophoresis (DGGE) and next generation sequencing on 16S rRNA gene region were combined and used as extent to each other. Two important clusters were characterized, where Staphylococcus and Corynebacterium species were the abundant species. Females predominantly clustered within the Staphylococcus cluster (87%, n = 17), whereas males clustered more in the Corynebacterium cluster (39%, n = 36). The axillary microbiota was unique to each individual. Left-right asymmetry occurred in about half of the human population. For the first time, an elaborate study was performed on the dynamics of the axillary microbiome. A relatively stable axillary microbiome was noticed, although a few subjects evolved towards another stable community. The deodorant usage had a proportional linear influence on the species diversity of the axillary microbiome.

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Conflict of interest statement

Competing Interests: The authors have declared that no competing interests exist.

Figures

Figure 1
Figure 1. Clustering of individual axillary samples analyzed by means of DGGE, where 69% of the subjects clustered into the Staphylococcus cluster and 31% into the Corynebacterium cluster.
Right: Subject indices from S1 till S53 (not all subjects were shown); gender of the subject; pyrosequenced samples indicated with MID (multiplex identifiers). Above: Identified band: bands A were identified as Staphylococcus epidermidis (100% identity), bands B were identified as Staphylococcus spp. (99% identity), bands C were identified as Staphylococcus hominis (100% identity), bands D and E were identified as Proteobacteria (from pyrosequencing results), band G was identified as Corynebacterium spp. (99% identity), bands H were identified as Corynebacterium spp. (99% identity), and bands F, I, J and K were identified as Corynebacterium spp. (from pyrosequencing results). Left: Clustering of the samples, based on Pearson correlation and unweighted pair group with mathematical averages dendrogram method. Under: indication of GC% of the bacterial bands. Firmicutes have a low GC%, and bands are generally situated left on the gel; Actinobacteria have a high GC%, with bands situated generally on the right side of the gel.
Figure 2
Figure 2. Dynamics (moving window analysis) of 7 subjects of the DGGE results.
LA = left axilla; RA = right axilla. Left axis indicates the similarity (based on Pearson correlation) of the axillary sample compared to the previous axillary sample. The higher the curve, the more similar the samples. The axillary microbiome was relatively constant throughout time, even on a longer timescale (9 months). Two followed-up subjects experienced an community shift from one cluster to the other, after which the microbiome again was stable (subject 4 and 11).
Figure 3
Figure 3. Stacked bar sample-wise taxonomic distribution of the sequences on genus level of the nine pyrosequenced axillary samples.
MID7 is a sample of a female person (34 y, S16) using deodorant 24 times per week; MID8 is a sample of a male person (24 y, S4) using no deodorant; MID10 is a sample of a male person (24 y, S28) using deodorant 7 times per week; MID11 is a sample of a male person (27 y, S11) using deodorant 5 times per week; MID13 is a sample of a male person (27 y, S27) using deodorant 3 times per week; MID14 is a sample of a male person (25 y, S38) using deodorant 3 times per week; MID15 is a sample of a male person (23 y, S6) using deodorant 7 times per week; MID16 is a sample of a male person (29 y, S31) using deodorant 10 times per week; MID17 is a sample of a male person (35 y, S1) using deodorant 7 times per week. All subjects were Belgian. Additional subject metadata description can be found in Table S7 in File S1.
Figure 4
Figure 4. Heatmap and clustering of the top 25 OTUs of the pyrosequenced samples.
Data was ranked according to the total count of the OTU among all the samples and samples were clustered using hierarchical clustering (complete linkage) and Bray-Curtis distance measures. OTU0001 = Corynebacterium spp., OTU0005 = Staphylococcus spp., OTU0002 = Moraxellaceae (Proteobacteria). Full OTU description can be found in Table S6 in File S1.
Figure 5
Figure 5. Rarefaction curve on the complete dataset of the pyrosequenced samples.
The normalization cut-off was set on 7135 sequences, as indicated by the vertical red line.
Figure 6
Figure 6. Shannon index for community diversity of all nine pyrosequenced samples in function of the subjects weekly deodorant use.
The higher the subjects deodorant usage frequency, the higher the diversity of the microbial community. The line is an indication of the proportional correlation between deodorant use and species diversity.
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Grants and funding

This work was supported through the assistantship of Chris Callewaert by the Flemish Government. Frederiek-Maarten Kerckhof was funded through the geconcerteerde onderzoeksactie (GOA) of Ghent University (BOF09/GOA/005). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.