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Cross-Disease Transcriptomics: Unique IL-17A Signaling in Psoriasis Lesions and an Autoimmune PBMC Signature

Open ArchivePublished:May 17, 2016DOI:https://doi.org/10.1016/j.jid.2016.04.035
      Transcriptome studies of psoriasis have identified robust changes in mRNA expression through large-scale analysis of patient cohorts. These studies, however, have analyzed all mRNA changes in aggregate, without distinguishing between disease-specific and nonspecific differentially expressed genes (DEGs). In this study, RNA-seq meta-analysis was used to identify (1) psoriasis-specific DEGs altered in few diseases besides psoriasis and (2) nonspecific DEGs similarly altered in many other skin conditions. We show that few cutaneous DEGs are psoriasis specific and that the two DEG classes differ in their cell type and cytokine associations. Psoriasis-specific DEGs are expressed by keratinocytes and induced by IL-17A, whereas nonspecific DEGs are expressed by inflammatory cells and induced by IFN-γ and tumor necrosis factor. Peripheral blood mononuclear cell-derived DEGs were more psoriasis specific than cutaneous DEGs. Nonetheless, peripheral blood mononuclear cell DEGs associated with major histocompatibility complex class I and natural killer cells were commonly downregulated in psoriasis and other autoimmune diseases (e.g., multiple sclerosis, sarcoidosis, and juvenile rheumatoid arthritis). These findings demonstrate “cross-disease” transcriptomics as an approach to gain insights into the cutaneous and noncutaneous psoriasis transcriptomes. This highlighted unique contributions of IL-17A to the cytokine network and uncovered a blood-based gene signature that links psoriasis to other diseases of autoimmunity.

      Abbreviations:

      DEG (differentially expressed gene), FC (fold-change), KC (keratinocyte), FPKM (fragments per kilobase of transcript per million mapped reads), MS (multiple sclerosis), NK (natural killer), PBMC (peripheral blood mononuclear cell), PN (uninvolved skin from patients with psoriasis), PP (lesional skin from patients with psoriasis), PSI (psoriasis specificity index), SCC (squamous cell carcinoma)

      Introduction

      Psoriasis is a cytokine-driven skin disease in which lesions develop because of abnormal epidermal thickening and keratinocyte (KC) hyperproliferation. Transcriptome studies of psoriasis have been performed using large patient cohorts to understand how gene expression is altered in lesional as compared with macroscopically normal skin (
      • Li B.
      • Tsoi L.C.
      • Swindell W.R.
      • Gudjonsson J.E.
      • Tejasvi T.
      • Johnston A.
      • et al.
      Transcriptome analysis of psoriasis in a large case-control sample: RNA-seq provides insights into disease mechanisms.
      ,
      • Swindell W.R.
      • Sarkar M.K.
      • Stuart P.E.
      • Voorhees J.J.
      • Elder J.T.
      • Johnston A.
      • et al.
      Psoriasis drug development and GWAS interpretation through in silico analysis of transcription factor binding sites.
      ,
      • Tian S.
      • Krueger J.G.
      • Li K.
      • Jabbari A.
      • Brodmerkel C.
      • Lowes M.A.
      • et al.
      Meta-analysis derived (MAD) transcriptome of psoriasis defines the “core” pathogenesis of disease.
      ). These studies have been instrumental for identifying genes (
      • Tian S.
      • Krueger J.G.
      • Li K.
      • Jabbari A.
      • Brodmerkel C.
      • Lowes M.A.
      • et al.
      Meta-analysis derived (MAD) transcriptome of psoriasis defines the “core” pathogenesis of disease.
      ), pathways, and transcription factors altered in lesional skin (
      • Swindell W.R.
      • Sarkar M.K.
      • Stuart P.E.
      • Voorhees J.J.
      • Elder J.T.
      • Johnston A.
      • et al.
      Psoriasis drug development and GWAS interpretation through in silico analysis of transcription factor binding sites.
      ), treatment response biomarkers (
      • Swindell W.R.
      • Johnston A.
      • Voorhees J.J.
      • Elder J.T.
      • Gudjonsson J.E.
      Dissecting the psoriasis transcriptome: inflammatory- and cytokine-driven gene expression in lesions from 163 patients.
      ), and for developing new bioinformatic methods (
      • Swindell W.R.
      • Johnston A.
      • Voorhees J.J.
      • Elder J.T.
      • Gudjonsson J.E.
      Dissecting the psoriasis transcriptome: inflammatory- and cytokine-driven gene expression in lesions from 163 patients.
      ). Nearly all prior psoriasis transcriptome studies, however, have focused exclusively on psoriasis, or psoriasis in combination with one or two other inflammatory conditions (e.g., atopic dermatitis) (
      • Choy D.F.
      • Hsu D.K.
      • Seshasayee D.
      • Fung M.A.
      • Modrusan Z.
      • Martin F.
      • et al.
      Comparative transcriptomic analyses of atopic dermatitis and psoriasis reveal shared neutrophilic inflammation.
      ,
      • Dhingra N.
      • Suarez-Farinas M.
      • Fuentes-Duculan J.
      • Gittler J.K.
      • Shemer A.
      • Raz A.
      • et al.
      Attenuated neutrophil axis in atopic dermatitis compared to psoriasis reflects TH17 pathway differences between these diseases.
      ,
      • Nomura I.
      • Gao B.
      • Boguniewicz M.
      • Darst M.A.
      • Travers J.B.
      • Leung D.Y.
      Distinct patterns of gene expression in the skin lesions of atopic dermatitis and psoriasis: a gene microarray analysis.
      ). Broader investigations seeking to establish connections between psoriasis and more distantly related skin conditions, such as skin cancers, have been less common (
      • Haider A.S.
      • Peters S.B.
      • Kaporis H.
      • Cardinale I.
      • Fei J.
      • Ott J.
      • et al.
      Genomic analysis defines a cancer-specific gene expression signature for human squamous cell carcinoma and distinguishes malignant hyperproliferation from benign hyperplasia.
      ). Transcriptomic features of psoriasis, however, overlap strongly with other skin conditions (e.g., squamous cell carcinoma [SCC], atopic dermatitis, acne), even though mechanisms governing the etiology of psoriasis are distinct (
      • Swindell W.R.
      • Stuart P.E.
      • Sarkar M.K.
      • Voorhees J.J.
      • Elder J.T.
      • Johnston A.
      • et al.
      Cellular dissection of psoriasis for transcriptome analyses and the post-GWAS era.
      ). This raises the question of whether aggregate analysis of psoriasis differentially expressed genes (DEGs) will inform our understanding of disease etiology, because such DEGs may reflect generic cutaneous responses that occur in many skin diseases in the absence of shared causal mechanisms (
      • D'Erme A.M.
      • Wilsmann-Theis D.
      • Wagenpfeil J.
      • Hölzel M.
      • Ferring-Schmitt S.
      • Sternberg S.
      • et al.
      IL-36gamma (IL-1F9) is a biomarker for psoriasis skin lesions.
      ,
      • Swindell W.R.
      • Remmer H.A.
      • Sarkar M.K.
      • Xing X.
      • Barnes D.H.
      • Wolterink L.
      • et al.
      Proteogenomic analysis of psoriasis reveals discordant and concordant changes in mRNA and protein abundance.
      ).
      Disease-specific and nonspecific signals in transcriptome data can be identified by “cross disease transcriptomics” (
      • Swindell W.R.
      • Remmer H.A.
      • Sarkar M.K.
      • Xing X.
      • Barnes D.H.
      • Wolterink L.
      • et al.
      Proteogenomic analysis of psoriasis reveals discordant and concordant changes in mRNA and protein abundance.
      ). This approach emphasizes comparison of expression data across disease states and draws on the wealth of transcriptome data archived in public databases (e.g., GEO) (
      • Rung J.
      • Brazma A.
      Reuse of public genome-wide gene expression data.
      ). Large-scale analyses of such data have not focused on psoriasis, but have nonetheless shed light on functional aspects of transcriptome overlap between psoriasis and other skin diseases (
      • Inkeles M.S.
      • Scumpia P.O.
      • Swindell W.R.
      • Lopez D.
      • Teles R.M.
      • Graeber T.G.
      • et al.
      Comparison of molecular signatures from multiple skin diseases identifies mechanisms of immunopathogenesis.
      ,
      • Wong D.
      • Kea B.
      • Pesich R.
      • Higgs B.W.
      • Zhu W.
      • Brown P.
      • et al.
      Interferon and biologic signatures in dermatomyositis skin: specificity and heterogeneity across diseases.
      ).
      • Wong D.
      • Kea B.
      • Pesich R.
      • Higgs B.W.
      • Zhu W.
      • Brown P.
      • et al.
      Interferon and biologic signatures in dermatomyositis skin: specificity and heterogeneity across diseases.
      identified genes differentially expressed between dermatomyositis and normal skin, and compared these with expression profiles from other skin conditions (e.g., lupus, atopic dermatitis). This showed that genes with similarly altered expression in psoriasis and other diseases are associated with epidermal barrier, interferon, and lipid metabolism (
      • Wong D.
      • Kea B.
      • Pesich R.
      • Higgs B.W.
      • Zhu W.
      • Brown P.
      • et al.
      Interferon and biologic signatures in dermatomyositis skin: specificity and heterogeneity across diseases.
      ). More recently,
      • Inkeles M.S.
      • Scumpia P.O.
      • Swindell W.R.
      • Lopez D.
      • Teles R.M.
      • Graeber T.G.
      • et al.
      Comparison of molecular signatures from multiple skin diseases identifies mechanisms of immunopathogenesis.
      compared microarray gene expression profiles from normal skin and 16 human skin diseases. Interestingly, cluster analysis grouped psoriasis expression profiles with neoplastic skin diseases (e.g., SCC and basal cell carcinoma) rather than prototypical inflammatory conditions (e.g., atopic dermatitis) (
      • Inkeles M.S.
      • Scumpia P.O.
      • Swindell W.R.
      • Lopez D.
      • Teles R.M.
      • Graeber T.G.
      • et al.
      Comparison of molecular signatures from multiple skin diseases identifies mechanisms of immunopathogenesis.
      ). These findings highlight proliferative and inflammatory processes shared between psoriasis and other diseases, which together contribute to transcriptome overlap and convolution of disease-specific and nonspecific signals (
      • Haider A.S.
      • Peters S.B.
      • Kaporis H.
      • Cardinale I.
      • Fei J.
      • Ott J.
      • et al.
      Genomic analysis defines a cancer-specific gene expression signature for human squamous cell carcinoma and distinguishes malignant hyperproliferation from benign hyperplasia.
      ).
      This study identifies psoriasis DEGs using RNA-seq meta-analysis and applies cross-disease transcriptomics to stratify DEGs based on psoriasis specificity. RNA-seq analysis of psoriasis lesions has been reported previously (
      • Jabbari A.
      • Suarez-Farinas M.
      • Dewell S.
      • Krueger J.G.
      Transcriptional profiling of psoriasis using RNA-seq reveals previously unidentified differentially expressed genes.
      ,
      • Keermann M.
      • Koks S.
      • Reimann E.
      • Prans E.
      • Abram K.
      • Kingo K.
      Transcriptional landscape of psoriasis identifies the involvement of IL36 and IL36RN.
      ,
      • Li B.
      • Tsoi L.C.
      • Swindell W.R.
      • Gudjonsson J.E.
      • Tejasvi T.
      • Johnston A.
      • et al.
      Transcriptome analysis of psoriasis in a large case-control sample: RNA-seq provides insights into disease mechanisms.
      ,
      • Swindell W.R.
      • Xing X.
      • Voorhees J.J.
      • Elder J.T.
      • Johnston A.
      • Gudjonsson J.E.
      Integrative RNA-seq and microarray data analysis reveals GC content and gene length biases in the psoriasis transcriptome.
      ,
      • Tsoi L.C.
      • Iyer M.K.
      • Stuart P.E.
      • Swindell W.R.
      • Gudjonsson J.E.
      • Tejasvi T.
      • et al.
      Analysis of long non-coding RNAs highlights tissue-specific expression patterns and epigenetic profiles in normal and psoriatic skin.
      ), but these data have not been integrated or compared systematically. We here integrate these data and identify protein-coding psoriasis DEGs based on the meta-dataset (n = 44 patients). We then discriminate between psoriasis-specific and nonspecific DEGs and characterize their respective functional properties and cell-type associations. With this approach, we illustrate a generally applicable strategy for skin disease transcriptome analysis. This disentangles disease-specific expression patterns from those generically observed in all skin conditions, leads to functional insights not discernable from aggregate analysis of all psoriasis DEGs, and represents one useful in silico pathway toward large-scale integration of gene expression data in dermatology.

      Results

      RNA-seq meta-analysis of lesional and uninvolved skin

      Meta-analysis was performed using RNA-seq reads generated from cDNA of paired lesional (PP) and uninvolved (PN) skin biopsies (GSE41745, GSE54456/GSE63979, and GSE66511; n = 44 patients) (
      • Jabbari A.
      • Suarez-Farinas M.
      • Dewell S.
      • Krueger J.G.
      Transcriptional profiling of psoriasis using RNA-seq reveals previously unidentified differentially expressed genes.
      ,
      • Li B.
      • Tsoi L.C.
      • Swindell W.R.
      • Gudjonsson J.E.
      • Tejasvi T.
      • Johnston A.
      • et al.
      Transcriptome analysis of psoriasis in a large case-control sample: RNA-seq provides insights into disease mechanisms.
      ,
      • Swindell W.R.
      • Remmer H.A.
      • Sarkar M.K.
      • Xing X.
      • Barnes D.H.
      • Wolterink L.
      • et al.
      Proteogenomic analysis of psoriasis reveals discordant and concordant changes in mRNA and protein abundance.
      ,
      • Tsoi L.C.
      • Iyer M.K.
      • Stuart P.E.
      • Swindell W.R.
      • Gudjonsson J.E.
      • Tejasvi T.
      • et al.
      Analysis of long non-coding RNAs highlights tissue-specific expression patterns and epigenetic profiles in normal and psoriatic skin.
      ). A total of 15,643 protein-coding genes satisfied our criteria as expressed features, with detectable expression in at least 25% of samples (see the Methods section). Samples from the three datasets differed with respect to the first fragments per kilobase of transcript per million mapped reads (FPKM) principal component axis (Supplementary Figure S1a online), but PP versus PN differences showed good agreement across data sources (Supplementary Figure S1b and c). Expected trends were observed among genes known to be altered in psoriasis lesions from previous work (Supplementary Figure S1b) (
      • Swindell W.R.
      • Sarkar M.K.
      • Stuart P.E.
      • Voorhees J.J.
      • Elder J.T.
      • Johnston A.
      • et al.
      Psoriasis drug development and GWAS interpretation through in silico analysis of transcription factor binding sites.
      ).
      RNA-seq meta-analysis fold-change (FC) estimates (n = 44 patients) were compared with those from a prior microarray data meta-analysis (n = 237 patients) (
      • Swindell W.R.
      • Sarkar M.K.
      • Stuart P.E.
      • Voorhees J.J.
      • Elder J.T.
      • Johnston A.
      • et al.
      Psoriasis drug development and GWAS interpretation through in silico analysis of transcription factor binding sites.
      ). RNA-seq meta-analysis FC estimates were highly correlated with those obtained by microarray (rs = 0.837; Supplementary Figure S2 online). For each RNA-seq dataset individually, however, FC estimates were less strongly correlated (0.678 ≤ rs ≤ 0.798; Supplementary Figure S2). Meta-analysis of RNA-seq data thus improved correspondence to microarray findings.

      RNA-seq meta-analysis identifies 2,113 differentially expressed protein-coding genes in psoriasis PP as compared with PN skin

      The 15,643 expressed genes were analyzed to assess evidence for differential expression (PP vs. PN skin), leading to the identification of 2,113 DEGs, including 954 PP-increased DEGs (FC > 2.0 with FDR < 0.05) and 1,159 PP-decreased DEGs (FC < 0.50 with FDR < 0.05). Most meta-analysis DEGs could be identified from analysis of at least one individual RNA-seq dataset, although some were uniquely identified by meta-analysis (e.g., PP-increased: CXCR5, BATF, PNMA5; PP-decreased: KRT13, FSTL5, GPC4; Supplementary Figure S3 online). Consistent with prior work (
      • Swindell W.R.
      • Xing X.
      • Voorhees J.J.
      • Elder J.T.
      • Johnston A.
      • Gudjonsson J.E.
      Integrative RNA-seq and microarray data analysis reveals GC content and gene length biases in the psoriasis transcriptome.
      ), expression of long genes tended to be decreased in PP skin (Supplementary Figure S4 online). There was no indication of GC content bias among DEGs identified from the pooled meta-dataset (Supplementary Figure S5 online), even though this was observed among DEGs from one individual dataset (i.e., GSE54456/GSE63979; Supplementary Figure S4). RNA-seq meta-analysis thus attenuated GC content bias.
      Genes with weak expression were more frequently identified as PP-decreased DEGs (Supplementary Figure S4c). For instance, among genes with mean FPKM < 0.20 (PP and PN samples), 21.1% were PP-decreased DEGs; in contrast, among genes with mean FPKM > 100, only 6.1% were PP-decreased DEGs (Supplementary Figure S4c). PP-decreased DEGs also showed weaker overlap with microarray findings as compared with PP-increased DEGs (Supplementary Figure S4d), with PP-decreased DEGs identified specifically by RNA-seq having relatively low expression (FPKM < 1.30 on average; Supplementary Figure S4e). Comparison of FC estimates between RNA-seq and microarray also supported weaker correspondence among low-expressed DEGs (Supplementary Figure S4f).

      Genes differentially expressed in psoriasis lesions overlap strongly with those altered in neoplastic and other inflammatory skin diseases

      We expected that many psoriasis DEGs would be nonspecific and similarly altered in other skin diseases. We thus screened 98 gene lists derived from microarray comparisons between diseased and normal skin (Supplementary Data File 1 online), with the goal of identifying diseases mirroring psoriasis (i.e., elevation of PP-increased DEGs; repression of PP-decreased DEGs). This revealed strong transcriptome-level correspondence between psoriasis and diverse skin conditions (P < 10−200), including Mediterranean spotted fever eschars, acne, infection, eczema, wounding, atopic dermatitis, and SCC (Figure 1a).
      Figure 1
      Figure 1Gene expression changes in psoriasis lesions overlap significantly with those in squamous cell carcinoma (SCC). (a) Skin diseases with psoriasis-like gene expression changes. Shifts in gene expression were quantified using signed log10-transformed P-values (Log10P; involved skin vs. control). Log10P was compared between PP-increased and PP-decreased DEGs (right margin, Wilcoxon rank-sum test). Red and blue bars span the middle 50% of Log10P-values among PP-increased and PP-decreased DEGs, respectively (gray boxes: non-DEGs). (b) FC comparison between SCC (GSE7553) and psoriasis (RNA-seq meta-analysis). (c) Overlap between PP-increased and SCC-increased DEGs. (d) Overlap between PP-decreased and SCC-decreased DEGs. Parts (e)(h) show genes similarly (e and f) and oppositely (g and h) altered in each disease (red/blue bars: FDR < 0.05). DEGs, differentially expressed genes; FC, fold-change; FDR, false discovery rate; PP, lesional skin from patients with psoriasis.
      SCC, for example, is etiologically distinct from psoriasis (
      • Arlette J.P.
      • Trotter M.J.
      Squamous cell carcinoma in situ of the skin: history, presentation, biology and treatment.
      ), but despite this, PP-increased DEGs were biased toward SCC-increased expression, and likewise, PP-decreased DEGs were biased toward SCC-decreased expression (Figure 1a). Genome-wide FCs also correlated between the two diseases (rs = 0.62; PP/N vs. SCC/N; Figure 1b). Genes elevated in both conditions included SERPINB4, S100A7A, and S100A9 (Figure 1e), whereas genes decreased in both conditions included DCD, SCGB2A2, and ADH1B (Figure 1f). Notably, IL17A was included among genes altered in psoriasis but not similarly altered in SCC (Figure 1g and h).

      A psoriasis specificity index discriminates between psoriasis-specific and nonspecific DEGs to highlight distinct gene set functional associations

      We next asked whether psoriasis-specific DEGs differ functionally from nonspecific DEGs. To address this, we developed a psoriasis specificity index (PSI) for DEG stratification (Figure 2), with lower values (PSI < 0) indicating relatively nonspecific DEGs and higher values (PSI ≈ 0 or PSI > 0) indicating relatively greater psoriasis specificity (Figure 2a). Applying this index indicated that most psoriasis DEGs are nonspecific (PSI < 0). Average PSI among PP-increased and PP-decreased DEGs, for example, was −1.44 (±0.023) and −0.86 (±0.014), respectively (Supplementary Figure S6 online). Highly nonspecific DEGs included S100A9 (PSI = −3.80), KRT16 (PSI = −3.37), BTC (PSI = −2.67), and TSPAN8 (PSI = −2.66) (Figure 2 and Supplementary Figure S7 online). Although few DEGs were strongly psoriasis specific (PSI ≈ 0 or PSI > 0), several DEGs with highest PSI encoded cytokines, for example, IL17A (PSI = −0.46), IL19 (PSI = −0.59), and IL36A (PSI = −0.73) (Figure 2c).
      Figure 2
      Figure 2Psoriasis specificity index (PSI) discriminates among cutaneous DEGs based on disease specificity. (a) PSI discriminates among DEGs with psoriasis-like gene expression shifts in other skin diseases (PSI < 0), DEGs unaltered in other skin diseases (PSI ≈ 0), and DEGs with psoriasis-opposite gene expression shifts in other diseases (PSI > 0). (b) Example PP-increased DEGs with low (top) and high (bottom) PSI and their expression shifts in other skin diseases. PSI for each gene is listed in parentheses (left margin). (c) Two hundred PP-increased DEGs with largest FC (PP/PN). DEGs were ranked from most (ARG1) to least (S100A9) psoriasis specific. The mean PSI of genes listed in each row is shown (left). (d) Two hundred PP-decreased DEGs with lowest FC (PP/PN). DEGs were ranked from most (CPNE6) to least (BTC) psoriasis specific. The mean PSI of genes listed in each row is shown (left). DEGs, differentially expressed genes; FC, fold-change; PN, uninvolved skin from patients with psoriasis; PP, lesional skin from patients with psoriasis.
      The 100 most nonspecific PP-increased DEGs (PSI ≤ −2.40) were enriched with respect to immune- and interferon-associated gene ontology biological process terms (Supplementary Figure S8b online). Genes driving this association included MX1, IFI27, IRF7, and OASL, all of which are commonly elevated in lesions from psoriasis and other diseases (Supplementary Figure S8b). Psoriasis-specific DEGs, in contrast, were frequently linked to membrane-associated processes, including transmembrane transport of calcium and potassium (Supplementary Figure S8a and c). Enrichment of immune and inflammation-associated gene ontology biological process terms among psoriasis DEGs thus appeared to be driven by nonspecific, rather than psoriasis-specific, DEGs.

      Increased gene expression in psoriasis lesions: nonspecific cutaneous inflammation and a psoriasis-specific KC response

      Gene expression in psoriasis partly reflects histological differences between lesional and normal skin (e.g., due to immune cell infiltration or epidermal expansion) (
      • Swindell W.R.
      • Johnston A.
      • Voorhees J.J.
      • Elder J.T.
      • Gudjonsson J.E.
      Dissecting the psoriasis transcriptome: inflammatory- and cytokine-driven gene expression in lesions from 163 patients.
      ). These differences have been analyzed using in silico methods (
      • Swindell W.R.
      • Johnston A.
      • Voorhees J.J.
      • Elder J.T.
      • Gudjonsson J.E.
      Dissecting the psoriasis transcriptome: inflammatory- and cytokine-driven gene expression in lesions from 163 patients.
      ), but such approaches have not distinguished among DEGs based on psoriasis specificity.
      We thus used PSI to order psoriasis DEGs and then used a “sliding window” approach to evaluate associations with 10 cell types. This showed that only nonspecific PP-increased DEGs are specifically expressed by inflammatory cells (e.g., monocytes and macrophages; Figure 3a). In contrast, psoriasis-specific PP-increased DEGs were KC specific (Figure 3a). Of the 100 most psoriasis-specific PP-increased DEGs, more than half were expressed most highly in KCs (Figure 3h). In contrast, many nonspecific PP-increased DEGs were expressed most strongly by inflammatory cells (e.g., neutrophils, monocytes, dendritic cell, and CD8+ T cells; Figure 3i). Distinctive features of the psoriasis transcriptome can thus be traced to KC-expressed genes, whereas immune cell infiltration appears to drive transcriptome convergence among skin diseases.
      Figure 3
      Figure 3Psoriasis-specific DEGs elevated in psoriasis lesions are KC associated. (a) Sliding window analysis. DEGs were ranked by PSI and windows were evaluated for enrichment with respect to each cell type (enrichment > 0: DEGs specifically expressed by cell type; 40 DEGs/window; right margin: trend score). (b, c) Cell-type enrichment for (b) psoriasis-specific and (c) nonspecific DEGs. (d) Cumulative overlap between PP-increased DEGs and genes ranked according to their level of KC-specific expression. (e) Sliding window analysis (PP-increased DEGs and KCs; see part a). (f, g) Cumulative overlap for (f) psoriasis-specific and (g) nonspecific DEGs. (h, i) Cell-type assignments for (h) psoriasis-specific and (i) nonspecific DEGs (**FDR < 0.05; Fisher’s exact test). DEGs, differentially expressed genes; FC, fold-change; FDR, false discovery rate; KC, keratinocyte; PP, lesional skin from patients with psoriasis; PSI, psoriasis specificity index.

      The unique psoriasis KC signature is associated with IL-17A but the nonspecific inflammatory signature is associated with IFN-γ and tumor necrosis factor

      Psoriasis-specific DEGs elevated in psoriasis lesions included cytokine-encoding genes (e.g., IL17A, IL19, and IL36A; Figure 2a). We thus screened 59 microarray experiments that measured KC expression responses after cytokine stimulation (Supplementary Data File 1), with the aim of identifying experiments in which psoriasis-specific and nonspecific DEGs responded differently to cytokine stimulation.
      Psoriasis-specific and nonspecific DEGs elevated in psoriasis lesions differed most strongly in their IL-17A response (Figure 4a). Psoriasis-specific PP-increased DEGs were disproportionately induced by IL-17A (e.g., ATP1B1, PRR9, PON2), whereas nonspecific DEGs were unaltered or in some cases repressed (e.g., S100A8, IFI27, STAT1) (Figure 4b–f). Using reverse transcription-PCR, we confirmed that psoriasis-specific DEGs (ATP1B1, PON2) are IL-17A induced, whereas nonspecific DEGs (KRT16, STAT1) show no induction or slight repression (Figure 4g). Reverse transcription-PCR also confirmed trends toward elevated ATP1B1 expression in psoriasis lesions (Figure 4h). Immunohistochemical staining localized ATP1B1 to lesional epidermis (Figure 4i and j), consistent with KC-specific expression (Figure 3).
      Figure 4
      Figure 4Psoriasis-specific DEGs elevated in lesions are IL17A induced. (a) Sliding window analysis. DEGs were ranked by PSI and windows were evaluated for enrichment with respect to each cytokine (enrichment > 0: DEGs cytokine induced; 40 DEGs/window; right margin: trend score). (b) Cumulative overlap between PP-increased DEGs and genes ranked by IL-17A response. (c) Sliding window analysis (PP-increased DEGs and IL-17A; see part a). (d, e) Cumulative overlap for (d) psoriasis-specific and (e) nonspecific DEGs. (f) Effects of IL-17A on PP-increased DEGs (GSE36287; top: psoriasis specific; bottom: nonspecific; parentheses: PSI). (g) Effects of IL-17A stimulation in cultured KCs (RT-PCR; n = 3). (h) ATP1B1 expression in PP, PN, and normal skin from control subjects (NN). (i, j) Immunohistochemical staining with an ATP1B1-specific antibody (bar: 100 μm). DEGs, differentially expressed genes; KC, keratinocyte; NN, nearest neighbor; PN, uninvolved skin from patients with psoriasis; PP, lesional skin from patients with psoriasis; PSI, psoriasis specificity index; RT-PCR, reverse transcription-PCR.
      Nonspecific DEGs elevated in psoriasis lesions were, in contrast, disproportionately induced by IFN-γ, tumor necrosis factor, and the combination IFN-γ + tumor necrosis factor, whereas this was not the case among psoriasis-specific DEGs (Figure 4a). Psoriasis-specific and nonspecific DEGs therefore exhibit differing cytokine responses, with enrichment for IL-17A targets observed only among psoriasis-specific DEGs.

      A peripheral blood mononuclear cell expression signature shared between psoriasis and other autoimmune diseases (multiple sclerosis, sarcoidosis, rheumatoid arthritis)

      We next considered whether DEGs identified from peripheral blood mononuclear cell (PBMC) from patients with psoriasis show greater psoriasis specificity than cutaneous DEGs. We therefore analyzed PBMC from patients with psoriasis (n = 5) and healthy controls (n = 5) (GSE40263), which led to the identification of 1,930 DEGs, including 693 psoriasis-increased DEGs and 1,237 psoriasis-decreased DEGs (P < 0.05). We examined how these DEGs were altered in 421 PBMC microarray comparisons (Supplementary Data File 1).
      PBMC-derived DEGs were more psoriasis specific than cutaneous DEGs, but still overlapped significantly with PBMC differential expression patterns in other diseases (Figure 5). We identified autoimmune diseases for which PBMC expression shifts mirrored those in psoriasis, including multiple sclerosis (MS) (P = 7.01 × 10−28), sarcoidosis (P = 4.54 × 10−13), juvenile rheumatoid arthritis (P = 4.81 × 10−25), and Crohn’s disease (P = 1.63 × 10−10) (Figure 5a). For each of these diseases, FC estimates (disease/control) were correlated with those calculated for psoriasis (psoriasis/control) (rs ≥ 0.46; Figure 5b–d).
      Figure 5
      Figure 5The psoriasis PBMC differential expression signature overlaps significantly with other autoimmune diseases. (a) PBMC treatments with psoriasis-like gene expression changes. Shifts in gene expression were quantified using signed log10-transformed P-values (Log10P). Log10P was compared between psoriasis-increased and psoriasis-decreased DEGs (right margin, Wilcoxon rank-sum test). Red and blue bars span the middle 50% of Log10P- values among psoriasis-increased and psoriasis-decreased DEGs, respectively (gray boxes: non-DEGs). Only the 100 psoriasis top-ranked DEGs were considered for each DEG group (selected based on FC). (b–d) FC comparison between psoriasis and (b) MS, (c) sarcoidosis, and (d) rheumatoid arthritis. (e–h) Genes similarly altered in psoriasis and (e, f) MS or (g, h) sarcoidosis (red/blue bars: FDR < 0.05). DEGs, differentially expressed genes; FC, fold-change; FDR, false discovery rate; MS, multiple sclerosis; PBMC, peripheral blood mononuclear cell.
      Genes decreased in both psoriasis and MS (Figure 5f), or both psoriasis and sarcoidosis (Figure 5h), were associated with antigen processing and presentation (via major histocompatibility complex class I) and interferon signaling (Figure 6a and b). DEGs decreased in patients with psoriasis and those with MS were specifically expressed by natural killer (NK) cells (Figure 6d), and such DEGs overlapped significantly with genes near (1–200 kb) psoriasis-associated single nucleotide polymorphisms from genome-wide association studies (Supplementary Figure S9e online).
      Figure 6
      Figure 6The PBMC signature common to psoriasis and other autoimmune diseases is enriched with MHC- and NK-cell-associated genes. (a) Gene ontology (GO) biological process (BP) terms enriched among genes with decreased expression in PBMC from patients with psoriasis and those with MS. (b) GO BP terms enriched among genes with decreased expression in PBMC from patients with psoriasis and those with sarcoidosis. (c, d) Cell type associated for genes (c) increased in PBMC from patients with psoriasis and those with MS or (d) decreased in PBMC from patients with psoriasis and those with MS (enrichment > 0: DEGs specifically expressed by cell type; enrichment < 0: DEGs not specifically expressed by cell type; red/blue: FDR < 0.05). Pie charts indicate the percentage of genes assigned to each cell type (*P < 0.05; **FDR < 0.05; Fisher’s exact test). DEGs, differentially expressed genes; FDR, false discovery rate; MHC, major histocompatibility complex; MS, multiple sclerosis; NK, natural killer; PBMC, peripheral blood mononuclear cell.

      Discussion

      Skin disease transcriptome studies have often followed a one-disease-at-a-time strategy without emphasizing comparisons with other disease states. This has been effective, but data sharing in the modern era now enables “cross-disease transcriptomics,” which is broader in scope, integrative, and capable of distinguishing between disease-specific and nonspecific mechanisms (
      • Haider A.S.
      • Peters S.B.
      • Kaporis H.
      • Cardinale I.
      • Fei J.
      • Ott J.
      • et al.
      Genomic analysis defines a cancer-specific gene expression signature for human squamous cell carcinoma and distinguishes malignant hyperproliferation from benign hyperplasia.
      ,
      • Inkeles M.S.
      • Scumpia P.O.
      • Swindell W.R.
      • Lopez D.
      • Teles R.M.
      • Graeber T.G.
      • et al.
      Comparison of molecular signatures from multiple skin diseases identifies mechanisms of immunopathogenesis.
      ,
      • Swindell W.R.
      • Remmer H.A.
      • Sarkar M.K.
      • Xing X.
      • Barnes D.H.
      • Wolterink L.
      • et al.
      Proteogenomic analysis of psoriasis reveals discordant and concordant changes in mRNA and protein abundance.
      ,
      • Wong D.
      • Kea B.
      • Pesich R.
      • Higgs B.W.
      • Zhu W.
      • Brown P.
      • et al.
      Interferon and biologic signatures in dermatomyositis skin: specificity and heterogeneity across diseases.
      ). We here used RNA-seq meta-analysis to identify DEGs in lesional psoriasis skin, but found that most DEGs are nonspecific and show similar differential expression in other skin conditions (Figure 1). Discrimination among DEGs using a PSI, however, identified IL-17A pathway activation as a unique feature of psoriasis lesions. This activation is accompanied by circulating immune cell (PBMC) responses paralleling those observed in other autoimmune diseases, which involve downregulation of major histocompatibility complex- and NK cell-associated genes (Figure 5, Figure 6). These findings highlight shared and distinctive features of psoriasis that can only be discerned from concurrent analysis of multiple diseases simultaneously. In future work, the integrative approach applied here can be extended to identify disease-specific drug targets, or potentially, to guide repurposing of existing drugs based on the knowledge of shared disease mechanisms.
      Psoriasis lesion development is closely tied to an underlying cytokine network, which directs and responds to signals from cutaneous cells, including KCs and infiltrating immunocytes (
      • Perera G.K.
      • Ainali C.
      • Semenova E.
      • Hundhausen C.
      • Barinaga G.
      • Kassen D.
      • et al.
      Integrative biology approach identifies cytokine targeting strategies for psoriasis.
      ). Previous psoriasis transcriptome studies have highlighted IL-1a, IL-1b, IL-17A, and IL-20R1/R2 cytokines (i.e., IL-19, IL-20, IL-24) as in vivo drivers of gene expression in psoriasis lesions (
      • Swindell W.R.
      • Johnston A.
      • Voorhees J.J.
      • Elder J.T.
      • Gudjonsson J.E.
      Dissecting the psoriasis transcriptome: inflammatory- and cytokine-driven gene expression in lesions from 163 patients.
      ,
      • Swindell W.R.
      • Johnston A.
      • Xing X.
      • Voorhees J.J.
      • Elder J.T.
      • Gudjonsson J.E.
      Modulation of epidermal transcription circuits in psoriasis: new links between inflammation and hyperproliferation.
      ,
      • Swindell W.R.
      • Stuart P.E.
      • Sarkar M.K.
      • Voorhees J.J.
      • Elder J.T.
      • Johnston A.
      • et al.
      Cellular dissection of psoriasis for transcriptome analyses and the post-GWAS era.
      ,
      • Yao Y.
      • Richman L.
      • Morehouse C.
      • de los Reyes M.
      • Higgs B.W.
      • Boutrin A.
      • et al.
      Type I interferon: potential therapeutic target for psoriasis?.
      ). Quantitative distinctions among these cytokines have been slight, however, based on enrichment of cytokine-induced gene sets among PP-increased DEGs. Our current analysis provides sharper distinction along these lines and identifies IL-17A as an inducer of DEGs most uniquely elevated in psoriasis lesions (Figure 4). Consistent with this, elevated IL17A mRNA was one of the most unique characteristics of psoriasis lesions (Figure 2). These findings agree with our previous analysis (focused on a subset of psoriasis DEGs) (
      • Swindell W.R.
      • Remmer H.A.
      • Sarkar M.K.
      • Xing X.
      • Barnes D.H.
      • Wolterink L.
      • et al.
      Proteogenomic analysis of psoriasis reveals discordant and concordant changes in mRNA and protein abundance.
      ), and also resonate with clinical data demonstrating efficacy of anti-IL-17A therapy for moderate-to-severe psoriasis (e.g., secukinumab, ixekizumab, brodalumab) (
      • Griffiths C.E.
      • Reich K.
      • Lebwohl M.
      • van de Kerkhof P.
      • Paul C.
      • Menter A.
      • et al.
      Comparison of ixekizumab with etanercept or placebo in moderate-to-severe psoriasis (UNCOVER-2 and UNCOVER-3): results from two phase 3 randomised trials.
      ,
      • Langley R.G.
      • Elewski B.E.
      • Lebwohl M.
      • Reich K.
      • Griffiths C.E.
      • Papp K.
      • et al.
      Secukinumab in plaque psoriasis—results of two phase 3 trials.
      ,
      • Lebwohl M.
      • Strober B.
      • Menter A.
      • Gordon K.
      • Weglowska J.
      • Puig L.
      • et al.
      Phase 3 studies comparing brodalumab with ustekinumab in psoriasis.
      ). This also provides proof-of-concept for the idea that cross-disease transcriptomics will point toward promising drug targets, suggesting that similar approaches might be applied to other skin diseases for which effective anticytokine treatments remain unknown.
      Transcriptome studies of psoriasis have increasingly utilized RNA-seq technology (
      • Jabbari A.
      • Suarez-Farinas M.
      • Dewell S.
      • Krueger J.G.
      Transcriptional profiling of psoriasis using RNA-seq reveals previously unidentified differentially expressed genes.
      ,
      • Keermann M.
      • Koks S.
      • Reimann E.
      • Prans E.
      • Abram K.
      • Kingo K.
      Transcriptional landscape of psoriasis identifies the involvement of IL36 and IL36RN.
      ,
      • Li B.
      • Tsoi L.C.
      • Swindell W.R.
      • Gudjonsson J.E.
      • Tejasvi T.
      • Johnston A.
      • et al.
      Transcriptome analysis of psoriasis in a large case-control sample: RNA-seq provides insights into disease mechanisms.
      ,
      • Swindell W.R.
      • Xing X.
      • Voorhees J.J.
      • Elder J.T.
      • Johnston A.
      • Gudjonsson J.E.
      Integrative RNA-seq and microarray data analysis reveals GC content and gene length biases in the psoriasis transcriptome.
      ,
      • Tsoi L.C.
      • Iyer M.K.
      • Stuart P.E.
      • Swindell W.R.
      • Gudjonsson J.E.
      • Tejasvi T.
      • et al.
      Analysis of long non-coding RNAs highlights tissue-specific expression patterns and epigenetic profiles in normal and psoriatic skin.
      ), but thus far meta-analysis has not been used to compare and integrate RNA-seq data generated from these studies. To our knowledge, this study provides the first meta-analysis of RNA-seq data from lesional psoriatic skin. Our results suggest good agreement between RNA-seq datasets and moderate-to-good correspondence to microarray findings (0.678 ≤ rs ≤ 0.798), with improved microarray correspondence obtained by pooling RNA-seq data across studies (rs = 0.837). This suggests that, as with microarrays, meta-analysis of RNA-seq data will be instrumental for establishing robust inferences based on the largest possible sample size (
      • Swindell W.R.
      • Sarkar M.K.
      • Stuart P.E.
      • Voorhees J.J.
      • Elder J.T.
      • Johnston A.
      • et al.
      Psoriasis drug development and GWAS interpretation through in silico analysis of transcription factor binding sites.
      ,
      • Tian S.
      • Krueger J.G.
      • Li K.
      • Jabbari A.
      • Brodmerkel C.
      • Lowes M.A.
      • et al.
      Meta-analysis derived (MAD) transcriptome of psoriasis defines the “core” pathogenesis of disease.
      ). Notably, however, low-expressed DEGs identified by RNA-seq were biased toward decreased expression. Given this trend, future RNA-seq studies of psoriasis may benefit from stringent detection thresholds to filter out low-expressed transcripts (
      • Swindell W.R.
      • Xing X.
      • Voorhees J.J.
      • Elder J.T.
      • Johnston A.
      • Gudjonsson J.E.
      Integrative RNA-seq and microarray data analysis reveals GC content and gene length biases in the psoriasis transcriptome.
      ). This may be especially important for studies focused on noncoding RNAs expressed at low levels, because a recent study noted greater differential expression among lncRNAs (as compared with protein-coding mRNAs) (
      • Tsoi L.C.
      • Iyer M.K.
      • Stuart P.E.
      • Swindell W.R.
      • Gudjonsson J.E.
      • Tejasvi T.
      • et al.
      Analysis of long non-coding RNAs highlights tissue-specific expression patterns and epigenetic profiles in normal and psoriatic skin.
      ). Our current findings suggest that this may reflect a systemic trend among all low-expressed features (protein coding and noncoding).
      Transcriptome analysis of PBMC and circulating leukocytes remains a relatively unexplored frontier in psoriasis. Most studies have focused on expression differences between lesional and normal skin (
      • Li B.
      • Tsoi L.C.
      • Swindell W.R.
      • Gudjonsson J.E.
      • Tejasvi T.
      • Johnston A.
      • et al.
      Transcriptome analysis of psoriasis in a large case-control sample: RNA-seq provides insights into disease mechanisms.
      ,
      • Swindell W.R.
      • Johnston A.
      • Voorhees J.J.
      • Elder J.T.
      • Gudjonsson J.E.
      Dissecting the psoriasis transcriptome: inflammatory- and cytokine-driven gene expression in lesions from 163 patients.
      ,
      • Swindell W.R.
      • Johnston A.
      • Xing X.
      • Voorhees J.J.
      • Elder J.T.
      • Gudjonsson J.E.
      Modulation of epidermal transcription circuits in psoriasis: new links between inflammation and hyperproliferation.
      ,
      • Swindell W.R.
      • Stuart P.E.
      • Sarkar M.K.
      • Voorhees J.J.
      • Elder J.T.
      • Johnston A.
      • et al.
      Cellular dissection of psoriasis for transcriptome analyses and the post-GWAS era.
      ,
      • Swindell W.R.
      • Sarkar M.K.
      • Stuart P.E.
      • Voorhees J.J.
      • Elder J.T.
      • Johnston A.
      • et al.
      Psoriasis drug development and GWAS interpretation through in silico analysis of transcription factor binding sites.
      ,
      • Tian S.
      • Krueger J.G.
      • Li K.
      • Jabbari A.
      • Brodmerkel C.
      • Lowes M.A.
      • et al.
      Meta-analysis derived (MAD) transcriptome of psoriasis defines the “core” pathogenesis of disease.
      ), but as we have shown, many such differences are nonspecific, inflammation associated, and generic. PBMC-derived DEGs, in contrast, overlap less prominently with other diseases, although we observed correspondence to autoimmune disease signatures (e.g., MS, sarcoidosis, and juvenile rheumatoid arthritis). Genes decreased in PBMC from both the patients with psoriasis and those with MS, in particular, are major histocompatibility complex class I associated, overlap significantly with genes near psoriasis-associated SNPs, and are NK cell specific (Figure 6). Loss of NK-cell-associated expression in PBMC from patients with psoriasis and those with MS appears consistent with NK-cell degeneration, as previously described for psoriasis and some other autoimmune diseases (
      • Cameron A.L.
      • Kirby B.
      • Griffiths C.E.
      Circulating natural killer cells in psoriasis.
      ,
      • Shi F.D.
      • Zhou Q.
      Natural killer cells as indispensable players and therapeutic targets in autoimmunity.
      ). This degeneration may be driven by cytokines released from autoreactive T cells (e.g., IL-21) (
      • Liu R.
      • Van Kaer L.
      • La Cava A.
      • Price M.
      • Campagnolo D.I.
      • Collins M.
      • et al.
      Autoreactive T cells mediate NK cell degeneration in autoimmune disease.
      ,
      • Shi F.D.
      • Van Kaer L.
      Reciprocal regulation between natural killer cells and autoreactive T cells.
      ). Our findings here demonstrate that this phenomenon can be analyzed based on the peripheral blood transcriptome of patients with autoimmune disease. Future large-cohort studies of the psoriasis PBMC transcriptome (and component PBMC cell types) may be beneficial for understanding mechanisms contributing to the shared autoimmune signature identified in this study.
      Most mRNA profiling studies of psoriasis have, thus far, focused on cutaneous lesions rather than the peripheral blood transcriptome (
      • Li B.
      • Tsoi L.C.
      • Swindell W.R.
      • Gudjonsson J.E.
      • Tejasvi T.
      • Johnston A.
      • et al.
      Transcriptome analysis of psoriasis in a large case-control sample: RNA-seq provides insights into disease mechanisms.
      ,
      • Swindell W.R.
      • Johnston A.
      • Voorhees J.J.
      • Elder J.T.
      • Gudjonsson J.E.
      Dissecting the psoriasis transcriptome: inflammatory- and cytokine-driven gene expression in lesions from 163 patients.
      ,
      • Swindell W.R.
      • Johnston A.
      • Xing X.
      • Voorhees J.J.
      • Elder J.T.
      • Gudjonsson J.E.
      Modulation of epidermal transcription circuits in psoriasis: new links between inflammation and hyperproliferation.
      ,
      • Swindell W.R.
      • Stuart P.E.
      • Sarkar M.K.
      • Voorhees J.J.
      • Elder J.T.
      • Johnston A.
      • et al.
      Cellular dissection of psoriasis for transcriptome analyses and the post-GWAS era.
      ,
      • Swindell W.R.
      • Sarkar M.K.
      • Stuart P.E.
      • Voorhees J.J.
      • Elder J.T.
      • Johnston A.
      • et al.
      Psoriasis drug development and GWAS interpretation through in silico analysis of transcription factor binding sites.
      ,
      • Tian S.
      • Krueger J.G.
      • Li K.
      • Jabbari A.
      • Brodmerkel C.
      • Lowes M.A.
      • et al.
      Meta-analysis derived (MAD) transcriptome of psoriasis defines the “core” pathogenesis of disease.
      ). Only a minority of cutaneous DEGs discovered by this approach, however, will approach a disease-specific expression pattern, and unique properties of such DEGs can be overlooked when all DEGs are analyzed in aggregate (
      • Swindell W.R.
      • Remmer H.A.
      • Sarkar M.K.
      • Xing X.
      • Barnes D.H.
      • Wolterink L.
      • et al.
      Proteogenomic analysis of psoriasis reveals discordant and concordant changes in mRNA and protein abundance.
      ). This is true for psoriasis lesions and, most likely, lesions from other skin diseases as well. The bioinformatic approach developed here provides one possible solution to this problem, in which a specificity index is first used to discriminate among DEGs, which then permits analysis of the most disease-specific DEGs apart from those that are nonspecific. We expect that similar disease-specificity indices can be applied in future studies of other skin disorders to provide insights not obtained from aggregate DEG analysis. This may highlight cytokines or pathways representing effective drug targets, as demonstrated here in the context of psoriasis and IL-17A (Figure 4). Furthermore, as new expression profiling datasets are generated by the skin research community, this approach should grow more powerful, ultimately serving to facilitate data integration, enhance bioinformatic method development, and define shared mechanisms among skin diseases with otherwise dissimilar appearance and etiology.

      Methods

      RNA-seq meta-analysis was performed using reads from prior studies of PP and PN skin (GSE41745, GSE54456/GSE63979, and GSE67785; n = 44 patients) (
      • Jabbari A.
      • Suarez-Farinas M.
      • Dewell S.
      • Krueger J.G.
      Transcriptional profiling of psoriasis using RNA-seq reveals previously unidentified differentially expressed genes.
      ,
      • Li B.
      • Tsoi L.C.
      • Swindell W.R.
      • Gudjonsson J.E.
      • Tejasvi T.
      • Johnston A.
      • et al.
      Transcriptome analysis of psoriasis in a large case-control sample: RNA-seq provides insights into disease mechanisms.
      ,
      • Swindell W.R.
      • Remmer H.A.
      • Sarkar M.K.
      • Xing X.
      • Barnes D.H.
      • Wolterink L.
      • et al.
      Proteogenomic analysis of psoriasis reveals discordant and concordant changes in mRNA and protein abundance.
      ,
      • Tsoi L.C.
      • Iyer M.K.
      • Stuart P.E.
      • Swindell W.R.
      • Gudjonsson J.E.
      • Tejasvi T.
      • et al.
      Analysis of long non-coding RNAs highlights tissue-specific expression patterns and epigenetic profiles in normal and psoriatic skin.
      ). Statistical methods were similar to a pipeline described previously (
      • Swindell W.R.
      • Xing X.
      • Voorhees J.J.
      • Elder J.T.
      • Johnston A.
      • Gudjonsson J.E.
      Integrative RNA-seq and microarray data analysis reveals GC content and gene length biases in the psoriasis transcriptome.
      ). TopHat was used to map reads to the UCSC hg19 genome (
      • Kim D.
      • Pertea G.
      • Trapnell C.
      • Pimentel H.
      • Kelley R.
      • Salzberg S.L.
      TopHat2: accurate alignment of transcriptomes in the presence of insertions, deletions and gene fusions.
      ). Read counts for protein-coding genes were then normalized using the voom algorithm (
      • Law C.W.
      • Chen Y.
      • Shi W.
      • Smyth G.K.
      voom: precision weights unlock linear model analysis tools for RNA-seq read counts.
      ), and differential expression was assessed using Bayesian linear models with moderated t-statistics (
      • Smyth G.K.
      Linear models and empirical Bayes methods for assessing differential expression in microarray experiments.
      ). DEGs identified by RNA-seq were cross-referenced with microarray-based gene signatures associated with human skin diseases (98 signatures), KC cytokine responses (59), and PBMC tissue (421) (Supplementary Data File 1).
      PSI was calculated using Equation (1) and P-values generated from n = 98 microarray comparisons involving human skin disease (diseased vs. normal skin).
      PSI=1n(j=1ndjlog10Pj)
      (1)


      The P-value from the jth experiment (Pj) was log10-transformed and multiplied by a directionality indicator (dj), defined as −1 (psoriasis-consistent expression shift) or 1 (psoriasis-inconsistent). Missing P-values were imputed using a nearest-neighbor strategy. A complete description of RNA-seq read processing, differential expression analysis, and PSI calculation is provided as Supplementary Materials online.

      Conflict of Interest

      The authors state no conflict of interest.

      Acknowledgments

      This work was supported by NIH K08 grant AR060802 (JEG), NIH RO1 grant AR069071 (JEG), Doris Duke Charitable Foundation grant 2013106 (JEG), the Babcock Endowment Fund (JEG), the A. Alfred Taubman Medical Research Institute (JEG), the Kenneth and Frances Eisenberg Emerging Scholar Award (JEG), and the American Skin Association Carson Family Research Scholar Award in Psoriasis (WRS).

      Supplementary Material

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