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Updated: May 9, 2026

Lymphocyte Isolation from Human Skin for Phenotypic Analysis and Ex Vivo Cell Culture
Published on: April 8, 2016
Dissecting the psoriasis transcriptome: inflammatory- and cytokine-driven gene expression in lesions from 163
William R Swindell1, Andrew Johnston, John J Voorhees
1Department of Dermatology, University of Michigan School of Medicine, Ann Arbor, MI 48109-2200, USA. wswindel@umich.edu
Background:
Psoriasis lesions are characterized by large-scale shifts in gene expression. Mechanisms that underlie differentially expressed genes (DEGs), however, are not completely understood. We analyzed existing datasets to evaluate genome-wide expression in lesions from 163 psoriasis patients. Our aims were to identify mechanisms that drive differential expression and to characterize heterogeneity among lesions in this large sample.
Results:
We identified 1233 psoriasis-increased DEGs and 977 psoriasis-decreased DEGs. Increased DEGs were attributed to keratinocyte activity (56%) and infiltration of lesions by T-cells (14%) and macrophages (11%). Decreased DEGs, in contrast, were associated with adipose tissue (63%), epidermis (14%) and dermis (4%). KC/epidermis DEGs were enriched for genes induced by IL-1, IL-17A and IL-20 family cytokines, and were also disproportionately associated with AP-1 binding sites. Among all patients, 50% exhibited a heightened inflammatory signature, with increased expression of genes expressed by T-cells, monocytes and dendritic cells. 66% of patients displayed an IFN-γ-strong signature, with increased expression of genes induced by IFN-γ in addition to several other cytokines (e.g., IL-1, IL-17A and TNF). We show that such differences in gene expression can be used to differentiate between etanercept responders and non-responders.
Conclusions:
Psoriasis DEGs are partly explained by shifts in the cellular composition of psoriasis lesions. Epidermal DEGs, however, may be driven by the activity of AP-1 and cellular responses to IL-1, IL-17A and IL-20 family cytokines. Among patients, we uncovered a range of inflammatory- and cytokine-associated gene expression patterns. Such patterns may provide biomarkers for predicting individual responses to biologic therapy.
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