Skip to main content

Thank you for visiting nature.com. You are using a browser version with limited support for CSS. To obtain the best experience, we recommend you use a more up to date browser (or turn off compatibility mode in Internet Explorer). In the meantime, to ensure continued support, we are displaying the site without styles and JavaScript.

  • Letter
  • Published:

Topographic diversity of fungal and bacterial communities in human skin

Abstract

Traditional culture-based methods have incompletely defined the microbial landscape of common recalcitrant human fungal skin diseases, including athlete’s foot and toenail infections. Skin protects humans from invasion by pathogenic microorganisms and provides a home for diverse commensal microbiota1. Bacterial genomic sequence data have generated novel hypotheses about species and community structures underlying human disorders2,3,4. However, microbial diversity is not limited to bacteria; microorganisms such as fungi also have major roles in microbial community stability, human health and disease5. Genomic methodologies to identify fungal species and communities have been limited compared with those that are available for bacteria6. Fungal evolution can be reconstructed with phylogenetic markers, including ribosomal RNA gene regions and other highly conserved genes7. Here we sequenced and analysed fungal communities of 14 skin sites in 10 healthy adults. Eleven core-body and arm sites were dominated by fungi of the genus Malassezia, with only species-level classifications revealing fungal-community composition differences between sites. By contrast, three foot sites—plantar heel, toenail and toe web—showed high fungal diversity. Concurrent analysis of bacterial and fungal communities demonstrated that physiologic attributes and topography of skin differentially shape these two microbial communities. These results provide a framework for future investigation of the contribution of interactions between pathogenic and commensal fungal and bacterial communities to the maintainenace of human health and to disease pathogenesis.

This is a preview of subscription content, access via your institution

Access options

Buy this article

Prices may be subject to local taxes which are calculated during checkout

Figure 1: Relative abundance of fungal genera and Malassezia species at different human skin sites.
Figure 2: Median richness of fungal and bacterial genera.
Figure 3: Forces that shape fungal and bacterial communities.
Figure 4: Clinical involvement alters shared fungal community structure.

Similar content being viewed by others

Accession codes

Accessions

GenBank/EMBL/DDBJ

Sequence Read Archive

Data deposits

Sequence data from this study have been submitted to GenBank/EMBL/DDBJ under accession numbers KC669797KC675175, and the Sequence Read Archive,and can be accessed through BioProject identification no.46333. Patient and sample metadata have been deposited in the controlled-access Database of Genotypes and Phenotypes (dbGaP) under study accession phs000266.v1.p1.

References

  1. Marples, M., ed. The Ecology of the Human Skin (Bannerstone House, 1965)

  2. Grice, E. A. & Segre, J. A. The human microbiome: our second genome. Annu. Rev. Genomics Hum. Genet. 13, 151–170 (2012)

    Article  CAS  Google Scholar 

  3. Human Microbiome Project Consortium. Structure, function and diversity of the healthy human microbiome. Nature 486, 207–214 (2012)

  4. Pflughoeft, K. J. & Versalovic, J. Human microbiome in health and disease. Annu. Rev. Pathol. 7, 99–122 (2012)

    Article  CAS  Google Scholar 

  5. Peleg, A. Y., Hogan, D. A. & Mylonakis, E. Medically important bacterial–fungal interactions. Nature Rev. Microbiol. 8, 340–349 (2010)

    Article  CAS  Google Scholar 

  6. Dollive, S. et al. A tool kit for quantifying eukaryotic rRNA gene sequences from human microbiome samples. Genome Biol. 13, R60 (2012)

    Article  Google Scholar 

  7. James, T. Y. et al. Reconstructing the early evolution of Fungi using a six-gene phylogeny. Nature 443, 818–822 (2006)

    Article  CAS  ADS  Google Scholar 

  8. Ghannoum, M. A. et al. Characterization of the oral fungal microbiome (mycobiome) in healthy individuals. PLoS Pathog. 6, e1000713 (2010)

    Article  Google Scholar 

  9. Paulino, L. C., Tseng, C. H., Strober, B. E. & Blaser, M. J. Molecular analysis of fungal microbiota in samples from healthy human skin and psoriatic lesions. J. Clin. Microbiol. 44, 2933–2941 (2006)

    Article  CAS  Google Scholar 

  10. Roth, R. R. & James, W. D. Microbial ecology of the skin. Annu. Rev. Microbiol. 42, 441–464 (1988)

    Article  CAS  Google Scholar 

  11. Bickers, D. R. et al. The burden of skin diseases: 2004 a joint project of the American Academy of Dermatology Association and the Society for Investigative Dermatology. J. Am. Acad. Dermatol. 55, 490–500 (2006)

    Article  Google Scholar 

  12. Gaitanis, G., Magiatis, P., Hantschke, M., Bassukas, I. D. & Velegraki, A. The Malassezia genus in skin and systemic diseases. Clin. Microbiol. Rev. 25, 106–141 (2012)

    Article  Google Scholar 

  13. Saunders, C. W., Scheynius, A. & Heitman, J. Malassezia fungi are specialized to live on skin and associated with dandruff, eczema, and other skin diseases. PLoS Pathog. 8, e1002701 (2012)

    Article  CAS  Google Scholar 

  14. Larone, D. H. Medically Important Fungi: A guide to identification (ASM Press, 2002)

    Google Scholar 

  15. St-Germain, G. & Summerbell, R. Identifying Fungi: A Clinical Laboratory Handbook (Star Publishing Company, 2011)

    Google Scholar 

  16. Gioti, A. et al. Genomic insights into the atopic eczema-associated skin commensal yeast Malassezia sympodialis. mBio 4, e00572–12 (2013)

    Article  CAS  Google Scholar 

  17. Bruns, T. D. et al. Evolutionary relationships within the fungi: analyses of nuclear small subunit rRNA sequences. Mol. Phylogenet. Evol. 1, 231–241 (1992)

    Article  CAS  Google Scholar 

  18. Schoch, C. L. et al. Nuclear ribosomal internal transcribed spacer (ITS) region as a universal DNA barcode marker for Fungi. Proc. Natl Acad. Sci. USA 109, 6241–6246 (2012)

    Article  CAS  ADS  Google Scholar 

  19. Quast, C. et al. The SILVA ribosomal RNA gene database project: improved data processing and web-based tools. Nucleic Acids Res. 41, D590–D596 (2013)

    Article  CAS  Google Scholar 

  20. Matsen, F. A., Kodner, R. B. & Armbrust, E. V. pplacer: linear time maximum-likelihood and Bayesian phylogenetic placement of sequences onto a fixed reference tree. BMC Bioinformatics 11, 538 (2010)

    Article  Google Scholar 

  21. Schloss, P. D. et al. Introducing mothur: open-source, platform-independent, community-supported software for describing and comparing microbial communities. Appl. Environ. Microbiol. 75, 7537–7541 (2009)

    Article  CAS  Google Scholar 

  22. Costello, E. K. et al. Bacterial community variation in human body habitats across space and time. Science 326, 1694–1697 (2009)

    Article  CAS  ADS  Google Scholar 

  23. Grice, E. A. et al. Topographical and temporal diversity of the human skin microbiome. Science 324, 1190–1192 (2009)

    Article  CAS  ADS  Google Scholar 

  24. Cohen, A. D., Wolak, A., Alkan, M., Shalev, R. & Vardy, D. A. Prevalence and risk factors for tinea pedis in Israeli soldiers. Int. J. Dermatol. 44, 1002–1005 (2005)

    Article  CAS  Google Scholar 

  25. Perea, S. et al. Prevalence and risk factors of tinea unguium and tinea pedis in the general population in Spain. J. Clin. Microbiol. 38, 3226–3230 (2000)

    CAS  PubMed  PubMed Central  Google Scholar 

  26. Iliev, I. D. et al. Interactions between commensal fungi and the C-type lectin receptor Dectin-1 influence colitis. Science 336, 1314–1317 (2012)

    Article  CAS  ADS  Google Scholar 

  27. Perfect, J. R., Lindsay, M. H. & Drew, R. H. Adverse drug reactions to systemic antifungals. Prevention and management. Drug Saf. 7, 323–363 (1992)

    Article  CAS  Google Scholar 

  28. Edgar, R. C., Haas, B. J., Clemente, J. C., Quince, C. & Knight, R. UCHIME improves sensitivity and speed of chimera detection. Bioinformatics 27, 2194–2200 (2011)

    Article  CAS  Google Scholar 

  29. Wang, Q., Garrity, G. M., Tiedje, J. M. & Cole, J. R. Naive Bayesian classifier for rapid assignment of rRNA sequences into the new bacterial taxonomy. Appl. Environ. Microbiol. 73, 5261–5267 (2007)

    Article  CAS  Google Scholar 

  30. Altschul, S. F., Gish, W., Miller, W., Myers, E. W. & Lipman, D. J. Basic local alignment search tool. J. Mol. Biol. 215, 403–410 (1990)

    Article  CAS  Google Scholar 

  31. Grice, E. A. et al. Topographical and temporal diversity of the human skin microbiome. Science 324, 1190–1192 (2009)

    Article  CAS  ADS  Google Scholar 

  32. Grice, E. A. et al. A diversity profile of the human skin microbiota. Genome Res. 18, 1043–1050 (2008)

    Article  CAS  Google Scholar 

  33. Lennon, N. J. et al. A scalable, fully automated process for construction of sequence-ready barcoded libraries for 454. Genome Biol. 11, R15 (2010)

    Article  Google Scholar 

  34. Li, W. & Godzik, A. Cd-hit: a fast program for clustering and comparing large sets of protein or nucleotide sequences. Bioinformatics 22, 1658–1659 (2006)

    Article  CAS  Google Scholar 

  35. Edgar, R. C., Haas, B. J., Clemente, J. C., Quince, C. & Knight, R. UCHIME improves sensitivity and speed of chimera detection. Bioinformatics 27, 2194–2200 (2011)

    Article  CAS  Google Scholar 

  36. Schloss, P. D. et al. Introducing mothur: open-source, platform-independent, community-supported software for describing and comparing microbial communities. Appl. Environ. Microbiol. 75, 7537–7541 (2009)

    Article  CAS  Google Scholar 

  37. Conlan, S., Kong, H. H. & Segre, J. A. Species-level analysis of DNA sequence data from the NIH Human Microbiome Project. PLoS ONE 7, e47075 (2012)

    Article  CAS  ADS  Google Scholar 

  38. Kong, H. H. et al. Temporal shifts in the skin microbiome associated with disease flares and treatment in children with atopic dermatitis. Genome Res. 22, 850–859 (2012)

    Article  CAS  Google Scholar 

  39. Yue, J. C. & Clayton, M. K. A similarity measure based on species proportions. Comm. Statist. Theory Methods 34, 2123–2131 (2005)

    Article  MathSciNet  Google Scholar 

  40. Ewing, B. & Green, P. Base-calling of automated sequencer traces using phred. II. Error probabilities. Genome Res. 8, 186–194 (1998)

    Article  CAS  Google Scholar 

  41. Ewing, B., Hillier, L., Wendl, M. C. & Green, P. Base-calling of automated sequencer traces using phred. I. Accuracy assessment. Genome Res. 8, 175–185 (1998)

    Article  CAS  Google Scholar 

  42. Pruesse, E. et al. SILVA: a comprehensive online resource for quality checked and aligned ribosomal RNA sequence data compatible with ARB. Nucleic Acids Res. 35, 7188–7196 (2007)

    Article  CAS  Google Scholar 

  43. Edgar, R. C. MUSCLE: multiple sequence alignment with high accuracy and high throughput. Nucleic Acids Res. 32, 1792–1797 (2004)

    Article  CAS  Google Scholar 

  44. Matsen, F. A., Kodner, R. B. & Armbrust, E. V. pplacer: linear time maximum-likelihood and Bayesian phylogenetic placement of sequences onto a fixed reference tree. BMC Bioinformatics 11, 538 (2010)

    Article  Google Scholar 

  45. Han, M. V. & Zmasek, C. M. phyloXML: XML for evolutionary biology and comparative genomics. BMC Bioinformatics 10, 356 (2009)

    Article  Google Scholar 

Download references

Acknowledgements

We thank J. Heitman, A. Amend, Y. Shea, M. Turner, I. Brownell and M. Udey for helpful discussions. We thank J. Fekecs for assistance with the figures. This work was supported by the US National Institutes of Health (NIH) NHGRI and NCI Intramural Research Programs, and in part by NIH grant no. 1K99AR059222 (to H.H.K.). Sequencing was funded by grants from the NIH (1UH2AR057504-01 and 4UH3AR057504-02).

Author information

Authors and Affiliations

Authors

Consortia

Contributions

K.F., H.H.K. and J.A.S. designed the outline of the study. D.S. and E.N. recruited human subjects and assisted H.H.K. in sample collection for the experiment. K.F. and J.Y. assembled and curated the fungal database. J.A.M. and C.D. prepared the clinical samples for sequencing. The members of the NIH Intramural Sequencing Center Comparative Sequencing program carried out sequencing. K.F., J.O., S.C. and M.P. analysed sequence data. K.F., H.H.K. and J.A.S. drafted the manuscript with specific contributions from J.O., J.Y. and S.C. All authors read and approved the final version of the manuscript.

Corresponding authors

Correspondence to Heidi H. Kong or Julia A. Segre.

Ethics declarations

Competing interests

The authors declare no competing financial interests.

Supplementary information

Supplementary Information

This file contains Supplementary Material and Methods, additional references, Supplementary Figures 1-10 and Supplementary Tables 1-8. (PDF 1904 kb)

PowerPoint slides

Rights and permissions

Reprints and permissions

About this article

Cite this article

Findley, K., Oh, J., Yang, J. et al. Topographic diversity of fungal and bacterial communities in human skin. Nature 498, 367–370 (2013). https://doi.org/10.1038/nature12171

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1038/nature12171

Search

Quick links

Nature Briefing

Sign up for the Nature Briefing newsletter — what matters in science, free to your inbox daily.

Get the most important science stories of the day, free in your inbox. Sign up for Nature Briefing