Characterization of the skin fungal microbiota in patients with atopic dermatitis and in healthy subjects

Authors


Takashi Sugita, Department of Microbiology, Meiji Pharmaceutical University, 2-522-1 Noshio, Kiyose, Tokyo 204-8588 Japan.
Tel: +81 45 367 6645; fax: +81 424 95 8762; email: sugita@my-pharm.ac.jp

ABSTRACT

Patients with atopic dermatitis (AD) are highly susceptible to viral, bacterial, and fungal skin infections because their skin is dry and this compromises the barrier function of the skin. Therefore, the skin microbiota of patients with AD is believed to be different from that of healthy individuals. In the present study, the skin fungal microbiota of nine patients with mild, moderate, or severe AD and ten healthy subjects were compared using an rRNA clone library. Fungal D1/D2 large subunit analysis of 3647 clones identified 58 species and seven unknown phylotypes in face scale samples from patients with AD and healthy subjects. Malassezia species were predominant, accounting for 63%–86% of the clones identified from each subject. Overall, the non-Malassezia yeast microbiota of the patients was more diverse than that of the healthy individuals. In the AD samples 13.0 ± 3.0 species per case were detected, as compared to 8.0 ± 1.9 species per case in the samples taken from healthy individuals. Notably, Candida albicans, Cryptococcus diffluens, and Cryptococcus liquefaciens were detected in the samples from the patients with AD. Of the filamentous fungal microbiota, Cladosporium spp. and Toxicocladosporium irritans were the predominant species in these patients. Many pathogenic fungi, including Meyerozyma guilliermondii (anamorphic name, Candida guilliermondii), and Trichosporon asahii, and allergenic microorganisms such as Alternaria alternata and Aureobasidium pullulans were found on the skin of the healthy subjects. When the fungal microbiota of the samples from patients with mild/moderate to severe AD and healthy individuals were clustered together by principal coordinates analysis they were found to be clustered according to health status.

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