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Single colonies were picked and re-streaked at least three times to isolate individual strains. Resulting forward and reverse reads were merged using the EMBOSS merger [ 38 ], and then merged reads were aligned using the assignment and classification functions at SINA [ 39 ] to available databases using default parameters Additional file 1 Table S3.
Low quality and unmerged reads were manually curated, and then forward and reverse reads were classified using BLAST. In addition to the 16S sequences from isolates, 16S sequences from 83 reference strains were added to help speciate isolate groups Additional file 1 Table S4.
To facilitate tree generation, 16S sequences that did not contain at least unambiguous nucleotides were removed. All sequences were then trimmed to the variable regions V2—V8 using the E. Isolates with more than 5 unidentifiable bases. Sequences were realigned with Infernal 1.
Initial tree making was run with FastTree version 2. Tree refinement was run with RAxML version 8. Trees were visualized using the r2d3 package in R. Molds were identified by tissue growth on plates and maintained by plug passaging, and yeasts were identified by microscopy and maintained as streak cultures.
For yeasts, lysing was replaced by colony PCR. PCR products were purified using magnetic beads then sequenced at Genewiz using the forward and reverse primers. Identification of fungal strains is included in Table S4. Skin-relevant compounds were selected based on a literature search for compounds detected in sweat, sebum, and as residual skin surface chemicals.
All skin-relevant compounds assessed, their sources and literature sources citing their presence on the skin are listed in Additional file 1 Table S6. Stock solutions were distributed into polypropylene well plates.
Negative controls consisted of three wells containing only water and three wells containing only chloroform. Water and chloroform were evaporated so that assay plates contained only 0. Bacteria pellets were washed three time by centrifugation at G for 10 min, aspiration of the supernatant, and gentle resuspension in an essential salt solution adapted from Bochner et al. The optical density at nm OD of the final bacterial solution was measured using a Nanodrop c spectrophotometer Thermo Scientific and bacteria were brought to an assay OD of 0.
Bacterial solution 0. At least three assay plates were examined for each bacterial isolate. Absorbance values from the 0 time point were subtracted from the 72 h values to yield background subtracted values. If positive control wells did not show growth for a bacterial isolate, the bacterial concentration was increased fold up to two times. Each set of assays included a plate with the essential salt solution and Biolog dye mix A without a bacterial inoculation to ensure sterility of the assay plate.
Compounds were classified using the BioCyc database [ 48 ], where the most specific parent class was chosen that allowed for molecule classifications with three or more compounds. Compounds not in the BioCyc database were classified with the ClassyFire tool [ 49 ].
Carbon source utilization was compared to the phylogenetic similarity of microbial taxa using two approaches: i Mantel tests and ii linear regression models. For both approaches, phylogenetic similarity was measured using phylogenetic distances calculated from the trimmed V2—V8 16S rRNA sequences described above and the similarity in carbon source utilization was measured using the Jaccard index.
For a given pair of microbial taxa, the Jaccard index equaled 1 if the two microbial taxa utilized the same set of carbon sources, 0 if they utilized completely different carbon sources, and values between 0 and 1 depending on the proportional overlap of carbon sources.
A Mantel test returns the correlation between two matrices, in this case, between a square matrix of phylogenetic similarity values each element was the phylogenetic similarity of a pair of microbial taxa and a corresponding square matrix of Jaccard similarity values each element was the Jaccard index for a pair of microbial taxa.
With linear regression models, Jaccard similarity was used as the response variable and phylogenetic similarity as the predictor variable. Because most pairs of microbial taxa did not share any carbon sources, linear regression models were also fit but excluding pairs of microbial taxa that did not share at least one carbon source.
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Download references. Finally, we would also like to acknowledge BEI Resources for agreeing to host a representative selection of strains for distribution to the scientific community.
This material is based upon work supported by the U. Army Research Laboratory and the U. Collin M. You can also search for this author in PubMed Google Scholar. CT designed the isolation study and performed and directed laboratory analyses, KL designed and performed metabolite usage studies, WS and TM performed 16S tree generation analyses, MP performed fungal ITS analyses, PS performed phylogenetic similarity analyses, CC performed human sampling, SN designed human sampling strategy, DK designed overall study and led the team.
All authors contributed to writing of the manuscript. The author s read and approved the final manuscript. Correspondence to David K. Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.
Reprints and Permissions. Timm, C. Isolation and characterization of diverse microbial representatives from the human skin microbiome. Microbiome 8, 58 Download citation. Received : 24 October Accepted : 18 March Published : 22 April Anyone you share the following link with will be able to read this content:.
Sorry, a shareable link is not currently available for this article. Provided by the Springer Nature SharedIt content-sharing initiative. Skip to main content. Search all BMC articles Search. Download PDF. Research Open Access Published: 22 April Isolation and characterization of diverse microbial representatives from the human skin microbiome Collin M.
Abstract Background The skin micro-environment varies across the body, but all sites are host to microorganisms that can impact skin health. Conclusions This collection is a resource that will support skin microbiome research with the potential for discovery of novel small molecules, development of novel therapeutics, and insight into the metabolic activities of the skin microbiota. Background Skin is a constantly growing and changing barrier tissue that acts as a first line of defense against many environmental factors.
Results Isolation conditions and counts We isolated microbial strains from the forehead, forearm, and antecubital fossa inner elbow to represent sebaceous, dry, and moist sites from 17 healthy volunteers. Full size image. Discussion In this study, we isolated and characterized over organisms from 3 body sites on 17 healthy research participants.
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