PT - JOURNAL ARTICLE AU - Alba Boix-Amorós AU - Michelle H Badri AU - Julia Manasson AU - Rebecca B Blank AU - Rebecca H Haberman AU - Andrea L Neimann AU - Parvathy V Girija AU - Anthony Jimenez Hernandez AU - Adriana Heguy AU - Sergei B Koralov AU - Richard Bonneau AU - Jose C Clemente AU - Jose U Scher TI - Alterations in the cutaneous microbiome of patients with psoriasis and psoriatic arthritis reveal similarities between non-lesional and lesional skin AID - 10.1136/ard-2022-223389 DP - 2023 Apr 01 TA - Annals of the Rheumatic Diseases PG - 507--514 VI - 82 IP - 4 4099 - http://ard.bmj.com/content/82/4/507.short 4100 - http://ard.bmj.com/content/82/4/507.full SO - Ann Rheum Dis2023 Apr 01; 82 AB - Objectives To investigate the cutaneous microbiome spanning the entire psoriatic disease spectrum, and to evaluate distinguishing features of psoriasis (PsO) and psoriatic arthritis (PsA).Methods Skin swabs were collected from upper and lower extremities of healthy individuals and patients with PsO and PsA. Psoriatic patients contributed both lesional (L) and contralateral non-lesional (NL) samples. Microbiota were analysed using 16S rRNA sequencing.Results Compared with healthy skin, alpha diversity in psoriatic NL and L skin was significantly reduced (p<0.05) and samples clustered separately in plots of beta diversity (p<0.05). Kocuria and Cutibacterium were enriched in healthy subjects, while Staphylococcus was enriched in psoriatic disease. Microbe–microbe association networks revealed a higher degree of similarity between psoriatic NL and L skin compared with healthy skin despite the absence of clinically evident inflammation. Moreover, the relative abundance of Corynebacterium was higher in NL PsA samples compared with NL PsO samples (p<0.05), potentially serving as a biomarker for disease progression.Conclusions These findings show differences in diversity, bacterial composition and microbe–microbe interactions between healthy and psoriatic skin, both L and NL. We further identified bacterial biomarkers that differentiate disease phenotypes, which could potentially aid in predicting the transition from PsO to PsA.Data are available in a public, open access repository. All sequence data will be made publicly available on publication at the NIH Sequence Read Archive (https://www.ncbi.nlm.nih.gov/sra). Additionally, code that was used to perform computational analyses in this manuscript will be made available at https://github.com/scher-lab.