Microbiomic signatures of psoriasis: Feasibility and methodology comparison

Alexander Statnikov, Alexander V. Alekseyenko, Zhiguo Li, Mikael Henaff, Guillermo I. Perez-Perez, Martin J. Blaser, Constantin F. Aliferis

Research output: Contribution to journalArticlepeer-review

65 Scopus citations

Abstract

Psoriasis is a common chronic inflammatory disease of the skin. We sought to use bacterial community abundance data to assess the feasibility of developing multivariate molecular signatures for differentiation of cutaneous psoriatic lesions, clinically unaffected contralateral skin from psoriatic patients, and similar cutaneous loci in matched healthy control subjects. Using 16S rRNA high-throughput DNA sequencing, we assayed the cutaneous microbiome for 51 such matched specimen triplets including subjects of both genders, different age groups, ethnicities and multiple body sites. None of the subjects had recently received relevant treatments or antibiotics.Wefound that molecular signatures for the diagnosis of psoriasis result in significant accuracy ranging from 0.75 to 0.89 AUC, depending on the classification task. We also found a significant effect of DNA sequencing and downstream analysis protocols on the accuracy of molecular signatures. Our results demonstrate that it is feasible to develop accurate molecular signatures for the diagnosis of psoriasis from microbiomic data.

Original languageEnglish (US)
Article number2620
JournalScientific reports
Volume3
DOIs
StatePublished - Sep 10 2013

Bibliographical note

Funding Information:
This research was supported in part by the grants UH2 AR057506-01S1 from the Human Microbiome Project, 1UL1 RR029893 from the National Center for Research Resources, and R01 LM011179-01A1 from the National Library of Medicine, National Institutes of Health; by the Diane Belfer Program in Human Microbial Ecology; and by the Department of Veterans Affairs Office of Research and Development. The authors thank Dr. Efstratios Efstathiadis and Dr. Eric Peskin for providing high performance computing support.

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