RT Journal Article
SR Electronic
T1 Alterations in the cutaneous microbiome of patients with psoriasis and psoriatic arthritis reveal similarities between non-lesional and lesional skin
JF Annals of the Rheumatic Diseases
JO Ann Rheum Dis
FD BMJ Publishing Group Ltd and European League Against Rheumatism
SP 507
OP 514
DO 10.1136/ard-2022-223389
VO 82
IS 4
A1 Boix-Amorós, Alba
A1 Badri, Michelle H
A1 Manasson, Julia
A1 Blank, Rebecca B
A1 Haberman, Rebecca H
A1 Neimann, Andrea L
A1 Girija, Parvathy V
A1 Jimenez Hernandez, Anthony
A1 Heguy, Adriana
A1 Koralov, Sergei B
A1 Bonneau, Richard
A1 Clemente, Jose C
A1 Scher, Jose U
YR 2023
UL http://ard.bmj.com/content/82/4/507.abstract
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.