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Atopic Dermatitis Is an IL-13–Dominant Disease with Greater Molecular Heterogeneity Compared to Psoriasis

Open ArchivePublished:January 11, 2019DOI:https://doi.org/10.1016/j.jid.2018.12.018
      Atopic dermatitis (AD) affects up to 20% of children and adults worldwide. To gain a deeper understanding of the pathophysiology of AD, we conducted a large-scale transcriptomic study of AD with deeply sequenced RNA-sequencing samples using long (126-bp) paired-end reads. In addition to the comparisons against previous transcriptomic studies, we conducted in-depth analysis to obtain a high-resolution view of the global architecture of the AD transcriptome and contrasted it with that of psoriasis from the same cohort. By using 147 RNA samples in total, we found striking correlation between dysregulated genes in lesional psoriasis and lesional AD skin with 81% of AD dysregulated genes being shared with psoriasis. However, we described disease-specific molecular and cellular features, with AD skin showing dominance of IL-13 pathways, but with near undetectable IL-4 expression. We also demonstrated greater disease heterogeneity and larger proportion of dysregulated long noncoding RNAs in AD, and illustrated the translational impact, including skin-type classification and drug-target prediction. This study is by far the largest study comparing the AD and psoriasis transcriptomes using RNA sequencing and demonstrating the shared inflammatory components, as well as specific discordant cytokine signatures of these two skin diseases.

      Abbreviations:

      AD (atopic dermatitis), DE (differential expression), DEG (differential expressed gene), RNA-seq (RNA sequencing), Th (T helper type)

      Introduction

      Atopic dermatitis (AD) is a common chronic inflammatory skin disease that affects up to 20% of children and adults of different populations (
      • DaVeiga S.P.
      Epidemiology of atopic dermatitis: a review.
      ). It has a complex genetic condition (
      • Paternoster L.
      • Standl M.
      • Waage J.
      • Baurecht H.
      • Hotze M.
      • Strachan D.P.
      • et al.
      Multi-ancestry genome-wide association study of 21,000 cases and 95,000 controls identifies new risk loci for atopic dermatitis.
      ) and is characterized by disturbed epidermal architecture and keratinocyte differentiation, as well as excessive T-cell–mediated inflammation (
      • Boehncke W.H.
      • Schon M.P.
      ,
      • Weidinger S.
      • Novak N.
      Atopic dermatitis.
      ). While these pathophysiological features are broadly shared with psoriasis, the clinical presentations are mutually exclusive, with opposing genetic effects in shared pathways (
      • Baurecht H.
      • Hotze M.
      • Brand S.
      • Buning C.
      • Cormican P.
      • Corvin A.
      • et al.
      Genome-wide comparative analysis of atopic dermatitis and psoriasis gives insight into opposing genetic mechanisms.
      ) and antagonistic immune deviations (
      • Eyerich S.
      • Onken A.T.
      • Weidinger S.
      • Franke A.
      • Nasorri F.
      • Pennino D.
      • et al.
      Mutual antagonism of T cells causing psoriasis and atopic eczema.
      ). While different groups have investigated the AD transcriptome using microarray technology (
      • 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.
      ,
      • Gittler J.K.
      • Shemer A.
      • Suarez-Farinas M.
      • Fuentes-Duculan J.
      • Gulewicz K.J.
      • Wang C.Q.
      • et al.
      Progressive activation of T(H)2/T(H)22 cytokines and selective epidermal proteins characterizes acute and chronic atopic dermatitis.
      ,
      • Quaranta M.
      • Knapp B.
      • Garzorz N.
      • Mattii M.
      • Pullabhatla V.
      • Pennino D.
      • et al.
      Intraindividual genome expression analysis reveals a specific molecular signature of psoriasis and eczema.
      ,
      • Rodriguez E.
      • Baurecht H.
      • Wahn A.F.
      • Kretschmer A.
      • Hotze M.
      • Zeilinger S.
      • et al.
      An integrated epigenetic and transcriptomic analysis reveals distinct tissue-specific patterns of DNA methylation associated with atopic dermatitis.
      ,
      • Suarez-Farinas M.
      • Ungar B.
      • Correa da Rosa J.
      • Ewald D.A.
      • Rozenblit M.
      • Gonzalez J.
      • et al.
      RNA sequencing atopic dermatitis transcriptome profiling provides insights into novel disease mechanisms with potential therapeutic implications.
      ), the sample sizes so far have been relatively small compared with those conducted for psoriasis. In addition, AD is a heterogeneous disease that has been proposed to comprise several distinct subtypes (
      • Thijs J.L.
      • Strickland I.
      • Bruijnzeel-Koomen C.
      • Nierkens S.
      • Giovannone B.
      • Csomor E.
      • et al.
      Moving toward endotypes in atopic dermatitis: Identification of patient clusters based on serum biomarker analysis.
      ,
      • Weidinger S.
      • Novak N.
      Atopic dermatitis.
      ). In contrast, transcriptomic studies, particularly RNA-sequencing (RNA-seq)–based, have been instrumental in identifying transcripts and pathways in psoriasis (
      • Gudjonsson J.E.
      • Ding J.
      • Johnston A.
      • Tejasvi T.
      • Guzman A.M.
      • Nair R.P.
      • et al.
      Assessment of the psoriatic transcriptome in a large sample: additional regulated genes and comparisons with in vitro models.
      ,
      • 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.
      ,
      • 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.
      ), establishing psoriasis as a T-helper type (Th)17/IL-23 and TNF-associated disease (
      • Greb J.E.
      • Goldminz A.M.
      • Elder J.T.
      • Lebwohl M.G.
      • Gladman D.D.
      • Wu J.J.
      • et al.
      ,
      • Varshney P.
      • Narasimhan A.
      • Mittal S.
      • Malik G.
      • Sardana K.
      • Saini N.
      Transcriptome profiling unveils the role of cholesterol in IL-17A signaling in psoriasis.
      ); and AD-based studies have been mostly limited to lower-resolution microarray-based studies (
      • 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.
      ,
      • Gittler J.K.
      • Shemer A.
      • Suarez-Farinas M.
      • Fuentes-Duculan J.
      • Gulewicz K.J.
      • Wang C.Q.
      • et al.
      Progressive activation of T(H)2/T(H)22 cytokines and selective epidermal proteins characterizes acute and chronic atopic dermatitis.
      ,
      • Guttman-Yassky E.
      • Suarez-Farinas M.
      • Chiricozzi A.
      • Nograles K.E.
      • Shemer A.
      • Fuentes-Duculan J.
      • et al.
      Broad defects in epidermal cornification in atopic dermatitis identified through genomic analysis.
      ,
      • 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.
      ,
      • Rodriguez E.
      • Baurecht H.
      • Wahn A.F.
      • Kretschmer A.
      • Hotze M.
      • Zeilinger S.
      • et al.
      An integrated epigenetic and transcriptomic analysis reveals distinct tissue-specific patterns of DNA methylation associated with atopic dermatitis.
      ,
      • Suarez-Farinas M.
      • Tintle S.J.
      • Shemer A.
      • Chiricozzi A.
      • Nograles K.
      • Cardinale I.
      • et al.
      Nonlesional atopic dermatitis skin is characterized by broad terminal differentiation defects and variable immune abnormalities.
      ). There have been limited RNA-seq–based small studies conducted on AD skin (
      • Suarez-Farinas M.
      • Ungar B.
      • Correa da Rosa J.
      • Ewald D.A.
      • Rozenblit M.
      • Gonzalez J.
      • et al.
      RNA sequencing atopic dermatitis transcriptome profiling provides insights into novel disease mechanisms with potential therapeutic implications.
      ), and a small number of microarray-based studies have attempted to disentangle the specific transcriptomic signatures of psoriasis versus AD (
      • 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.
      ,
      • Guttman-Yassky E.
      • Suarez-Farinas M.
      • Chiricozzi A.
      • Nograles K.E.
      • Shemer A.
      • Fuentes-Duculan J.
      • et al.
      Broad defects in epidermal cornification in atopic dermatitis identified through genomic analysis.
      ,
      • 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.
      ,
      • Quaranta M.
      • Knapp B.
      • Garzorz N.
      • Mattii M.
      • Pullabhatla V.
      • Pennino D.
      • et al.
      Intraindividual genome expression analysis reveals a specific molecular signature of psoriasis and eczema.
      ). Here, we performed RNA-seq by sequencing in high-depth the transcriptomes of 147 samples from carefully matched and tightly defined cohorts of AD patients, psoriasis patients, and healthy controls, whom were recruited within an ongoing investigator-initiated clinical study to identify shared and distinct disease mechanisms of AD and psoriasis. We hypothesized that by integrating deep sequencing–based transcriptome profiling with systems biology analysis, we are able to provide deep characterization for the expression signatures for AD, and by including psoriasis samples in the analysis, we can reveal the distinct molecular features of uninvolved and lesional skin of AD that have not been described previously.

      Results

       Transcriptomic architecture and components of AD

      Our cohort provides the largest number of co-sequenced AD and psoriasis samples of uninvolved and lesional skin to date (Supplementary Tables S1–S3 online) and obtained on average >31 million read (126 bp) pairs per sample. With 80% of uniquely mapped reads, we detected expression of 31,364 genes. While the first two principal components computed using the whole transcriptome can separate the normal/uninvolved skin versus lesional skin samples, they were not adequate to provide clean separation for psoriasis and AD lesional skin (Supplementary Figure S1 online). However, the top three principal components could provide a near perfect separation between the different lesional skin types (Figure 1a). AD has been regarded a heterogeneous clinical condition (
      • Weidinger S.
      • Novak N.
      Atopic dermatitis.
      ), and we evaluated the transcriptomic heterogeneity using the top principal components and demonstrate that the AD lesional skin presents a higher degree of heterogeneity than psoriasis (Figure 1b). By using psoriasis lesional skin as a reference, these results provide molecular and mechanistic support to complement previous clinical- or genetic-based observations for the heterogeneity of AD (
      • Cole C.
      • Kroboth K.
      • Schurch N.J.
      • Sandilands A.
      • Sherstnev A.
      • O'Regan G.M.
      • et al.
      Filaggrin-stratified transcriptomic analysis of pediatric skin identifies mechanistic pathways in patients with atopic dermatitis.
      ,
      • Weidinger S.
      • Novak N.
      Atopic dermatitis.
      ).
      Figure thumbnail gr1
      Figure 1Transcriptomes of different skin types. (a) The top three principal components for the samples in the cohort; (b) Euclidean distance (in logarithmic scale) distributions between all pairwise samples within the AD and psoriasis skin conditions. The distance was computed using the top three principal components; (c) Venn diagram showing the overlap between the differentially expressed genes in lesional skin; (d) number of differentially expressed genes in lesional skin; (e, f) scatter plots illustrating the concordance between lesional (e) and nonlesional (f) skin for psoriasis and AD, coloring genes differentially expressed in both axis (red), only in the x-axis (orange), or only in the y-axis (blue); (g) proportion of differentially expressed genes under each category for various differential expression conditions. AD, atopic dermatitis; CO, control; DEG, differentially expressed gene; FC, fold-change; lncRNA, long noncoding RNA; PSO, psoriasis.
      Differential expression (DE) (Supplementary Table S4 online) analyzes were conducted to identify differentially expressed genes (DEGs) (false discovery rate ≤5% and |log2 (fold change)|≥1). Our results are concordant with previous studies in psoriasis (
      • 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.
      ,
      • 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.
      ) that demonstrated large number of DEGs (up/down = 2,502/4,261) when comparing normal versus psoriasis lesional skin (Figure 1c). The DE analysis for normal versus AD lesional skin revealed a substantially larger number of DEGs (up/down = 1,529/1,921) compared to previous studies (
      • Gittler J.K.
      • Shemer A.
      • Suarez-Farinas M.
      • Fuentes-Duculan J.
      • Gulewicz K.J.
      • Wang C.Q.
      • et al.
      Progressive activation of T(H)2/T(H)22 cytokines and selective epidermal proteins characterizes acute and chronic atopic dermatitis.
      ,
      • Rodriguez E.
      • Baurecht H.
      • Wahn A.F.
      • Kretschmer A.
      • Hotze M.
      • Zeilinger S.
      • et al.
      An integrated epigenetic and transcriptomic analysis reveals distinct tissue-specific patterns of DNA methylation associated with atopic dermatitis.
      ,
      • Suarez-Farinas M.
      • Tintle S.J.
      • Shemer A.
      • Chiricozzi A.
      • Nograles K.
      • Cardinale I.
      • et al.
      Nonlesional atopic dermatitis skin is characterized by broad terminal differentiation defects and variable immune abnormalities.
      ,
      • Suarez-Farinas M.
      • Ungar B.
      • Correa da Rosa J.
      • Ewald D.A.
      • Rozenblit M.
      • Gonzalez J.
      • et al.
      RNA sequencing atopic dermatitis transcriptome profiling provides insights into novel disease mechanisms with potential therapeutic implications.
      ), suggesting that the sample size and the deep-sequencing can enhance the statistical power for more robust characterization of AD lesional skin. In fact, when comparing the changes in AD skin measured in previous microarray studies, we demonstrated the limited ability of microarray technology to detect low-expressing transcripts (Supplementary Figure S2 online). For instance, IL13, an upregulated cytokine in AD, could only be identified as significant in our study, but not in previous microarray experiments.
      Strikingly, we identified a large number of DEGs (n = 2,800) shared between AD and psoriasis lesional skin (Figure 1d; corresponding to 41% and 81% of the psoriasis and AD DEGs, respectively), and the correlations of the magnitudes in dysregulation in lesional skin in both diseases were significantly correlated (Figure 1e; Spearman ρ = 0.8; P < 1 × 10–16). In contrast, direct comparison between AD versus psoriasis lesional skin showed lower number of DEGs (up/down = 1,259/771) than those revealed in the normal versus lesional comparison. The DE results for comparing nonlesional with lesional AD skin illustrate similar findings (Supplementary Table S4). Despite modest differences between normal and nonlesional skin of psoriasis (also vs. non-lesional skin of AD), the dysregulation in uninvolved skin was significantly correlated with those in lesional skin (ρ = 0.55 and 0.64 for psoriasis and AD, respectively; P < 1 × 10–16), suggesting that nonlesional skin exhibit subtle pro-inflammatory responses and epidermal dysregulation that are further manifested in lesional skin. Interestingly, we also observed significant correlation (ρ = 0.4; P < 1 × 10–16) between the magnitudes of dysregulation in the psoriatic and AD non-lesional skin (Figure 1f). These evaluations identified gene profiles that mark progression from normal to uninvolved to inflamed skin and illustrated a surprisingly high degree of concordance between the gene expression in lesional AD and psoriatic skin.
      We demonstrated previously that a higher proportion of long noncoding RNAs tend to be differentially expressed in the psoriatic lesional skin than protein-coding genes, probably due to differences in cellular compositions (
      • 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.
      ). We observed the same trend in this study (i.e., 30% long noncoding RNA vs. 21% protein-coding are DEGs) (Supplementary Figure S3 online). Interestingly, a higher proportion of long noncoding RNAs tended to be differentially expressed in lesional AD skin (17% vs. 11%). These results were not influenced by the large number of DEGs overlapping between AD and psoriasis skin, as we still demonstrated consistent results in both AD-only and psoriasis-only DEGs (Figure 1g). Notably, while “Immunoglobin/T-cell receptor” was the gene category with the largest proportion of DEGs in any of the control versus lesional skin comparisons, it represents the smallest proportion in the direct psoriasis versus AD DE analysis, suggesting that the diseases share overlapping inflammatory pathways and responses. Despite otherwise distinct clinical features of AD and psoriasis, these two diseases have significant overlap in their molecular architecture, particularly in categories related to inflammatory responses.

       Common and specific molecular and cellular features of AD and psoriasis

      We identified 469 significant functions enriched in the common DEGs for lesional skin in AD/psoriasis, including functions associated with immunologic responses, and IFN and cytokine signaling pathways (top functions in Figure 2a; full list in Supplementary Table S5 online). When comparing the enrichment results between the AD-only versus the psoriasis-only DEGs, we observed distinct patterns of significance (Figure 2b). Functions involved in growth factor activity and myeloid dendritic cell activation were enriched in AD-only DEGs (Supplementary Table S5), while genes involved in functions like major histocompatibility complex class I antigen processing/presentation, cell cycle, and arginine/proline metabolism were enriched in psoriasis-only DEGs (Figure 2c). Nerve growth factor levels have been shown to reflect the severity of itching and eruption in AD (
      • Yamaguchi J.
      • Aihara M.
      • Kobayashi Y.
      • Kambara T.
      • Ikezawa Z.
      Quantitative analysis of nerve growth factor (NGF) in the atopic dermatitis and psoriasis horny layer and effect of treatment on NGF in atopic dermatitis.
      ), and perturbation of proline/arginine metabolism has been demonstrated in psoriasis (
      • Kamleh M.A.
      • Snowden S.G.
      • Grapov D.
      • Blackburn G.J.
      • Watson D.G.
      • Xu N.
      • et al.
      LC-MS metabolomics of psoriasis patients reveals disease severity-dependent increases in circulating amino acids that are ameliorated by anti-TNFalpha treatment.
      ). Functions that were enriched among genes dysregulated in the direct AD versus psoriasis comparison included “carbohydrate derivative binding,” “collagen metabolic process” (Supplementary Table S5), among the significant functions encompassing AD-specific DEGs included activity and composition of “chloride channels,” “regulation of fibroblast growth factor receptor signaling pathway,” “regulation of macrophage activation,” and “GABA ERK1/2 receptor activity” (Figure 2d). Interestingly, recent experimental data indicate that dysregulation of epidermal chloride channels (
      • Seltmann K.
      • Meyer M.
      • Sulcova J.
      • Kockmann T.
      • Wehkamp U.
      • Weidinger S.
      • et al.
      Humidity-regulated CLCA2 protects the epidermis from hyperosmotic stress.
      ), fibroblast growth factor receptor signaling (
      • Sulcova J.
      • Meyer M.
      • Guiducci E.
      • Feyerabend T.B.
      • Rodewald H.R.
      • Werner S.
      Mast cells are dispensable in a genetic mouse model of chronic dermatitis.
      ), as well as P38/ERK MAPK signaling pathways (
      • Tan Q.
      • Yang H.
      • Liu E.
      • Wang H.
      P38/ERK MAPK signaling pathways are involved in the regulation of filaggrin and involucrin by IL17.
      ), all impact epidermal barrier function and cutaneous homoeostasis, and might be involved in the pathogenesis of AD.
      Figure thumbnail gr2
      Figure 2Functional analysis for DEGs. (a) Biologic functions with corresponding genes significantly overlapped (-log[P value] for each function is shown) against genes commonly differentially expressed in AD and psoriatic lesional skin; (b) functional enrichment results between the psoriasis-only (x-axis) versus AD-only (y-axis) DEGs, each point represents one function; (c) top significant functions enriched among genes differentially expressed only in psoriatic lesional skin but not in AD lesional skin; (d) significant functions encompassing genes differentiating AD and psoriasis, and the genes are differentially expressed AD lesional skin, but not in psoriatic skin. AD, atopic dermatitis; DEG, differentially expressed gene; Psor, psoriasis.
      The DE analysis demonstrated overlap between the transcriptomes of nonlesional skin from AD and psoriasis patients (Figure 1f), when each was compared to normal control skin. Interestingly, each of the AD and psoriatic non-lesional skin DE gene sets were enriched for 32 significant functions/pathways, and >50% (17) of these overlapped, including “keratinocytes differentiation” (P ≤ 5 × 10–11) and “cytokine activity” (P ≤ 1 × 10–3) (Supplementary Table S6 online). Interestingly, for “keratinocyte differentiation” the same genes (i.e., LCE3A/D, S100A7) were dysregulated in the same direction in both types of nonlesional skin, with S100A7 and other precursor genes of cornified cell envelope (e.g., SPRR2A/B) having a higher expression in psoriasis nonlesional skin than AD nonlesional skin, and late cornified genes (i.e., LCE3A/D) showing no difference between the two nonlesional types. While both nonlesional skin types have alterations in common genes associated with keratinocyte differentiation, the changes were greater in psoriasis. For “cytokine activity,” different dysregulated genes were involved in psoriasis nonlesional (CCL4, IL19, IL36A/G) and AD nonlesional (CCL13, CCL18, IL13, IL20, IL26).
      We evaluated the profiles for 33 cytokines expressed in our skin samples to further characterize the inflammatory signatures (Figure 3). Cytokines encoding IL-17 (IL17A/F), IL-36 (IL36A/G), and IFN responses (IFNG) had the most prominent expression in lesional psoriatic skin (at least sevenfold more highly expressed in psoriasis compared to AD, respectively). It is noteworthy that IL-36, which has been studied in psoriasis extensively (
      • Mahil S.K.
      • Catapano M.
      • Di Meglio P.
      • Dand N.
      • Ahlfors H.
      • Carr I.M.
      • et al.
      An analysis of IL-36 signature genes and individuals with IL1RL2 knockout mutations validates IL-36 as a psoriasis therapeutic target.
      ), is also elevated in AD lesional skin (Figure 3, Supplementary Figure S4 online). IL-20 family cytokines, which have been shown to contribute to epidermal proliferation (i.e., IL19, IL20, IL22, IL24) were elevated in both AD and psoriasis. The Th2 signature gene, IL13, was the most distinctive markers for AD. In strong contrast to IL13, mRNA expression of IL4 was detectable in only 40% of the AD lesional samples, and at very low expression levels (Supplementary Table S7 online), suggesting that AD is an IL-13– rather than IL-4–driven disease (
      • Tazawa T.
      • Sugiura H.
      • Sugiura Y.
      • Uehara M.
      Relative importance of IL-4 and IL-13 in lesional skin of atopic dermatitis.
      ).
      Figure thumbnail gr3
      Figure 3Gene expressions for cytokines. (a) Heatmap illustrates the expression levels of different cytokines (rows; stratified by their families) across different samples (columns; stratified by different skin types); (b) boxplots to illustrate the expression distributions of six cytokines across the different skin conditions. AD, atopic dermatitis; CO, control; PSO, psoriasis.
      We then analyzed the enrichment of DEGs near different immune cell-specific active enhancer marks (defined by H3K27ac) using recent large-scale epigenomic data sets (
      • Farh K.K.
      • Marson A.
      • Zhu J.
      • Kleinewietfeld M.
      • Housley W.J.
      • Beik S.
      • et al.
      Genetic and epigenetic fine mapping of causal autoimmune disease variants.
      ,
      • Kundaje A.
      • Meuleman W.
      • Ernst J.
      • Bilenky M.
      • Yen A.
      • et al.
      Roadmap Epigenomics Consortium
      Integrative analysis of 111 reference human epigenomes.
      ) to infer cell type origins for these transcripts (Figure 4a, Supplementary Table S8 online). The shared DEGs in the lesional skin of AD and psoriasis were highly enriched in Th1/Th2/Th17/monocyte marks, while the Th2 epigenomic signature was enriched among the AD-only DEGs. The nearby enhancers of regulatory T and memory T cells were enriched in the common AD/psoriasis DEGs, while no enrichment in naïve T cells was observed. Among the DEGs in the direct psoriasis versus AD comparison, enrichment for CD8+ memory cells in both the AD-elevated and the psoriasis-elevated genes were observed separately. Using quantitative immunohistochemistry (Figure 4b; Supplementary Figure S5 online and Supplementary Table S9 online), we illustrated that the proportions of CD8+ and CD3+ cells in lesional psoriasis skin were significantly increased compared to normal skin (P = 0.01). Although we did not observe a significant increase in CD8+ cells in lesional AD skin when comparing against control or nonlesional AD skin, the presence of CD8+ cells and CD8+ chromatin signature (Figure 4) are consistent with previous suggestions that CD8+ T cells may play important roles in inflammatory cytokine production in AD, as well as psoriasis (
      • Hijnen D.
      • Knol E.F.
      • Gent Y.Y.
      • Giovannone B.
      • Beijn S.J.
      • Kupper T.S.
      • et al.
      CD8(+) T cells in the lesional skin of atopic dermatitis and psoriasis patients are an important source of IFN-gamma, IL-13, IL-17, and IL-22.
      )
      Figure thumbnail gr4
      Figure 4Cell type signatures in different skin types. (a) Heatmap illustrates the enrichment (negative logarithm of enrichment P value) of differentially expressed genes (columns for different comparisons) against nearby H3k27ac marks specific in different immune cells (rows). False discovery rate ≤5%; (b) proportions (%) of CD8 cells under different skin types (in logarithmic scale). AD, atopic dermatitis; CO, control; PSO, psoriasis.
      Using independent RNA-seq generated from keratinocytes stimulated by different cytokines, we correlated the gene expression responses against our disease DEGs (Figure 5a). When correlating cytokine responses in keratinocytes against the direct psoriatic versus AD lesional skin comparison, genes with higher expressions in psoriasis tend to be induced by IL-17A, IL-17A/TNF, IL-36α, β, γ, and IFN-α; while genes with higher expressions in AD tend to be induced by IL-4 and IL-13. These results indicate that while both diseases might have contribution from IL-17A/IL-36/TNF/IFN-α responses, the magnitude and the degree of these responses are less intense in AD, and vice versa for IL-4/IL-13. In fact, by using IL-17A, IL-4, and IL-13 responses as features, we could separate AD from psoriasis (Figure 5b). Notably, the uninvolved skin samples from both AD and psoriasis moved toward the same cytokine–response axes as the affected AD and psoriatic skin, respectively.
      Figure thumbnail gr5
      Figure 5Cytokine-induced signatures. (a) Heatmap shows the correlation between fold changes of dysregulated genes under cytokine-stimulated keratinocytes (rows) and fold changes of the differential expression analysis conducted in this study (columns). False discovery rate ≤5% and correlation coefficient ≥0.25; (b) the scatter plot projects how each sample responds to IL-17A, IL-13, and IL-4 stimulations. AD, atopic dermatitis; LS, lesional; NL, nonlesional; PSO, psoriasis.

       Transcriptomic analysis to provide translational inference for AD

      We were able to provide near perfect classification for AD and psoriasis (area under the receiver operating curve = 0.99 for left out data set) using only the IL17A, IL13, and IL36G expression profiles (Figure 6a), the classification performance concords with results from previous studies using the expressions of NOS2 and CCL27 as biomarkers (
      • Garzorz-Stark N.
      • Krause L.
      • Lauffer F.
      • Atenhan A.
      • Thomas J.
      • Stark S.P.
      • et al.
      A novel molecular disease classifier for psoriasis and eczema.
      ,
      • Quaranta M.
      • Knapp B.
      • Garzorz N.
      • Mattii M.
      • Pullabhatla V.
      • Pennino D.
      • et al.
      Intraindividual genome expression analysis reveals a specific molecular signature of psoriasis and eczema.
      ). Despite the modest differences between healthy skin and nonlesional AD skin, as well as healthy skin versus nonlesional psoriatic skin, we could obtain good classification: area under the receiver operating curve = 0.86 for AD uninvolved skin using IL13, EBI3, IL26, IL20, IL5, IL36A, IL36G, and area under the receiver operating curve = 0.91 for psoriasis uninvolved skin using IL36G, IL19, IL18, IL36A, EBI3, IL13 (Supplementary Table S10 online). These results reinforce the presence of a “pre-inflammatory” signature in both uninvolved AD and psoriasis skin. Notably, while both IL17A and IL36G were upregulated in psoriatic lesional skin, only IL36G, but not IL17A, was selected as one of the features differentiating normal from psoriatic nonlesional skin. Indeed, despite concordance between psoriasis uninvolved skin and IL17A induced effect in Figure 5a, IL36G, but not IL17A, was significantly upregulated in nonlesional psoriatic skin.
      Figure thumbnail gr6
      Figure 6Translational implications of this transcriptomic study. (a) Area under receiver operating characteristic to evaluate the performance of classifying different skin types using machine learning approach; (b, c) for each gene, the effect size in control versus lesional skin comparison (x-axis) versus its Spearman correlation between the expression level and severity index (y-axis); (d) for each gene, the comparison between the PASI correlation (x-axis) versus SCORAD correlation (y-axis). Black line indicates lowess fit using all genes (gray); red line represents lowess fit using only common differentially expressed genes (blue) in both AD and psoriatic lesional skin). AD, atopic dermatitis; cont, control; FC, fold-change; PASI, Psoriasis Area Severity Index; Pso, psoriasis.
      We hypothesized that with our larger transcriptomic cohort in AD lesional skin profiled using the higher-resolution RNA sequencing technology, we would obtain more powerful associations for disease severity compared with previous work (
      • Tejasvi T.
      • Tsoi L.C.
      • Stuart P.E.
      • Nair R.P.
      • Voorhees J.J.
      • Abecasis G.R.
      • et al.
      Transcriptome analysis reveals that severity of psoriasis is correlated with expression of genes involved in epidermal differentiation and proliferation.
      ). Analyzing expression of cytokines change with the severity index (Psoriasis Area Severity Index for psoriasis; SCORAD for AD) (
      • Oranje A.P.
      Practical issues on interpretation of scoring atopic dermatitis: SCORAD Index, objective SCORAD, patient-oriented SCORAD and Three-Item Severity score.
      ) together revealed that IL17A, IL23A, IL27, and EBI3 were correlated (P < 0.05) with Psoriasis Area Severity Index (all but IL27 was positively correlated), and IL13 and TSLP were positively correlated (P < 0.05) with SCORAD (Supplementary Figure S6 online and Supplementary Table S11 online). While these correlations are only nominally significant, we observed strong global correlations when associating the expression changes in lesional skin and the severity indices across the whole transcriptome for both associations (Figure 6b, 6c; Spearman correlation ρ = 0.4; P < 2 × 10–16), an observation that has not been described previously. Notably, the two Spearman correlation values from the above calculations were also highly correlated, particularly for the common AD/psoriasis DEGs (ρ = 0.3 for whole genome vs. ρ = 0.6 for common DEGs; Figure 6d).
      Finally, we investigated current medication gene targets for AD, and evaluated for enrichment among the different sets of DEGs. This demonstrated that existing drug targets for AD were highly enriched among DEGs in AD skin (P < 5 × 10–14; Supplementary Figure S7 online, Supplementary Table S12 online). Extending this analysis to other drugs and determining whether their gene targets were enriched with AD DEGs, we observed enrichment for several steroid agents (budesonide, triamcinolone, beclomethasone, alclometasone dipropionate) (false discovery rate <1%; observed to expected ratio ≥ 4). Most notably, tofacitinib, a Jak inhibitor, which has recent success in treating AD (
      • Bissonnette R.
      • Papp K.A.
      • Poulin Y.
      • Gooderham M.
      • Raman M.
      • Mallbris L.
      • et al.
      Topical tofacitinib for atopic dermatitis: a phase IIa randomized trial.
      ), proved to be highly significant (P = 5.4 × 10–18; observed to expected ratio = 4.9). These results demonstrate that transcriptomic data can be a valuable resource to reveal novel drug targets.

      Discussion

      AD is associated with epidermal barrier dysfunction, for example, due to mutations in the FLG gene (
      • O'Regan G.M.
      • Sandilands A.
      • McLean W.H.
      • Irvine A.D.
      Filaggrin in atopic dermatitis.
      ), type 1 allergic responses, with an increased susceptibility towards both bacterial and viral infections (
      • Weidinger S.
      • Beck L.A.
      • Bieber T.
      • Kabashima K.
      • Irvine A.D.
      Atopic dermatitis.
      ,
      • Wollenberg A.
      • Wetzel S.
      • Burgdorf W.H.
      • Haas J.
      Viral infections in atopic dermatitis: pathogenic aspects and clinical management.
      ). In addition, AD is also associated with atopic features, such as asthma and allergic rhinitis. In line with this, 25% of the AD patients studied here carried an FLG mutation, and 60% suffered from comorbid asthma and/or rhinitis (Supplementary Table S2). In contrast, psoriasis typically is not associated with a general skin barrier dysfunction or allergic features and is relatively resistant to skin infection. Although these two diseases are readily distinguished using clinical criteria, it has been suggested that AD and psoriasis exist on a “spectrum” (
      • Guttman-Yassky E.
      • Krueger J.G.
      Atopic dermatitis and psoriasis: two different immune diseases or one spectrum?.
      ) and amendable to shared treatment approaches (
      • Guttman-Yassky E.
      • Krueger J.G.
      • Lebwohl M.G.
      Systemic immune mechanisms in atopic dermatitis and psoriasis with implications for treatment.
      ) based on shared immunologic processes and the observation that with increased chronicity AD takes on histologic features that resemble psoriasis with associated prominence of Th1/IFN-γ responses (
      • Guttman-Yassky E.
      • Nograles K.E.
      • Krueger J.G.
      Contrasting pathogenesis of atopic dermatitis and psoriasis—part I: clinical and pathologic concepts.
      ). Our study provides insights into the shared and unique molecular features of psoriasis and AD; although the analyses revealed overlap of transcripts relevant for inflammation in both unaffected and affected skin, we identified distinct sets of gene features that clearly separate both diseases, such as IL-13/IL-4 response in AD versus IL-17 responses in psoriasis.
      AD has for a long time been considered a Th2 disease characterized by IL-4 cytokine responses (
      • Leung D.Y.
      • Bieber T.
      Atopic dermatitis.
      ). A surprising finding was that the mRNA expression of IL4 was detectable in only 40% of AD samples. While we found prominent enrichment for “IL-4 responses” in AD skin (Figure 5), IL-4 has a short in vivo half-life, and shares much of its biology with the cytokine IL-13, and IL-13 shares the heterodimeric receptor IL-4R/IL-13Rα with IL-4 (
      • Mueller T.D.
      • Zhang J.L.
      • Sebald W.
      • Duschl A.
      Structure, binding, and antagonists in the IL-4/IL-13 receptor system.
      ). It is likely that the “IL-4 signature” is largely attributable to IL-13 and related to the excessive amounts of IL-13 in AD skin (Figure 3). In fact, IL-4/IL-13 inhibition through blockade of the IL-4R by dupilumab has been shown in several studies to be a highly effective treatment for AD (
      • Blauvelt A.
      • de Bruin-Weller M.
      • Gooderham M.
      • Cather J.C.
      • Weisman J.
      • Pariser D.
      • et al.
      Long-term management of moderate-to-severe atopic dermatitis with dupilumab and concomitant topical corticosteroids (LIBERTY AD CHRONOS): a 1-year, randomised, double-blinded, placebo-controlled, phase 3 trial.
      ,
      • Simpson E.L.
      • Bieber T.
      • Guttman-Yassky E.
      • Beck L.A.
      • Blauvelt A.
      • Cork M.J.
      • et al.
      Two Phase 3 trials of dupilumab versus placebo in atopic dermatitis.
      ), whereas no clinical trials directly targeting IL-4 in AD have ever been published. Consistent with our findings, anti–IL-13 targeted therapy with tralokinumab, led to early and sustained improvement in AD symptoms (
      • Wollenberg A.
      • Howell M.D.
      • Guttman-Yassky E.
      • Silverberg J.I.
      • Kell C.
      • Ranade K.
      • et al.
      Treatment of atopic dermatitis with tralokinumab, an anti-IL-13 mAb.
      ).
      We acknowledge that AD is a heterogeneous disease and thus transcriptomic profiling results can vary as lesions evolve. In this study, we recruited only adult patients with a chronic AD/psoriasis using established criteria (for AD, the American Academy of Dermatology Consensus Criteria). Only AD lesions with a reported duration of >72 hours corresponding to the subacute to chronic state were selected in order to minimize potential differences between “early” versus “established” lesions. Future study that can consider the dynamics and fluctuations of the AD lesion through longitudinal assessments can have potential to provide a more thorough understanding of the disease trajectory, as well as its heterogeneous nature.
      In summary, this study provides a detailed view of the mechanisms operating in psoriasis and AD and suggests that AD is primarily an IL-13–dominant disease. Our data demonstrate a shared “core transcriptome” shared between AD and psoriasis, primarily characterized by enriched Th1/IFN responses. However, therapeutics targeting those shared processes have been largely disappointing in AD. Given the opposing polarization signal in AD (toward IL-13 responses) and psoriasis (toward IL-17 responses), our data are consistent with the notion that these diseases represent overlapping, yet distinct diseases, and do not fit the view that these diseases exist on an extended spectrum.

      Material and Methods

       Sample preparation

      Informed written consent was obtained from human subjects under a protocol approved by the local ethics board at the University Hospital Schleswig-Holstein, Kiel, Germany (reference: A100/12). AD or psoriasis was diagnosed on the basis of a skin examination by experienced dermatologists according to standard criteria (for AD, the American Academy of Dermatology Consensus Criteria were used). Five-millimeter skin punch biopsies from the extremities (under local anesthesia) were obtained. Total RNA was isolated from PAXgene fixed tissue samples using the AllPrep DNA/RNA Mini Kit (Qiagen, Hilden, Germany) following the manufacturer’s specifications. Preceding RNA isolation all samples were disrupted using innuSPEED Lysis Tubes W (1.4- to 1.6-mm steel beads and 3.5-mm ceramic beads) in a SpeedMill Plus (3 × 1-minute intervals) together with 600 μl RLT-Plus-Buffer (Qiagen) including β-mercaptoethanol and additionally homogenized with QIAshredder spin-columns. Only samples with a concentration of >50 ng/μl, an optical density 260/280 ≥1.8, and a RNA integrity number >7 were included in subsequent library preparation and sequencing. RNA samples were prepared for sequencing using the Illumina Truseq Stranded total RNA Protocol in combination with the Ribo-Zero rRNA Removal Kit (Illumina) and sequenced on the HiSeq2500 in pools of 10 samples with 2 × 125 bp.

       RNA-seq data analysis

      Illumina (San Diego, CA) standard primers were trimmed, and the quality of the data was assessed using FastQC, version 0.11.3 (
      • Andrews S.
      FastQC: a quality control tool for high throughput sequence data.
      ). Paired reads were mapped to the human reference genome (b37) using STAR (
      • Dobin A.
      • Davis C.A.
      • Schlesinger F.
      • Drenkow J.
      • Zaleski C.
      • Jha S.
      • et al.
      STAR: ultrafast universal RNA-seq aligner.
      ), only uniquely mapped read pairs were retained. Number of reads for each gene was counted using HTSeq (
      • Anders S.
      • Pyl P.T.
      • Huber W.
      HTSeq—a Python framework to work with high-throughput sequencing data.
      ), only genes with on average ≥1 read/sample were used in our analysis. Trimmed mean of M-values (TMM) was used to normalize the RNA-seq data (
      • Robinson M.D.
      • Oshlack A.
      A scaling normalization method for differential expression analysis of RNA-seq data.
      ), and we applied voom transformation to model the mean-variance relationship of the expression data (
      • Law C.W.
      • Chen Y.
      • Shi W.
      • Smyth G.K.
      voom: Precision weights unlock linear model analysis tools for RNA-seq read counts.
      ). We conducted differential expression analysis between different conditions using empirical Bayes linear model as implemented in the limma package (
      • Ritchie M.E.
      • Phipson B.
      • Wu D.
      • Hu Y.
      • Law C.W.
      • Shi W.
      • et al.
      limma powers differential expression analyses for RNA-sequencing and microarray studies.
      ). We identified cell-type specificity using H3K27ac peak signals (
      • Farh K.K.
      • Marson A.
      • Zhu J.
      • Kleinewietfeld M.
      • Housley W.J.
      • Beik S.
      • et al.
      Genetic and epigenetic fine mapping of causal autoimmune disease variants.
      ) and cytokine signatures using different cytokine stimulated keratinocytes transcriptome profiles. The details of the data and approaches are available in the Supplementary Material online.

      ORCID

      Conflict of Interest

      Johann E. Gudjonsson serves as Advisory Board for Novartis and MiRagen, and has received research support from AbbVie, SunPharma, and Genentech. Spiro Getsios is an employee and stakeholder of GlaxoSmithKline, and this study is in no way influenced by the work at GlaxoSmithKline. The remaining authors state no conflict of interest.

      Acknowledgments

      This work was supported by an award from the Else Kröner-Fresenius-Stiftung (2014_A270) (SW, ER), by the Deutsche Forschungsgemeinschaft (German Research Foundation) under Germany’s excellence Strategy–EXC 22167-390884018, by grants from the German Federal Ministry of Education and Research (BMBF) within the framework of the e:Med research and funding concept (sysINFLAME, grant 01ZX1306A) and the ComorbSysMed, grant 01ZX1510 (SS), and by the Babcock Endowment Fund (LCT, MKS, JT, JEG), by the National Institute of Arthritis and Musculoskeletal and Skin Diseases: AR060802 (JEG), AR054966 (JTE), and AR072129 (LCT), and National Institute of Allergy and Infectious Diseases under Award Number R01-AR06 (JEG), and the A. Alfred Taubman Medical Research Institute Kenneth and Frances Eisenberg Emerging Scholar Award (JEG). LCT is supported by the Dermatology Foundation, Arthritis National Research Foundation, and National Psoriasis Foundation.
      Data accession: National Center for Biotechnology Information Gene Expression Omnibus accession: GSE121212.

      Author Contributions

      SW designed the study. SW and NV contributed with clinical samples and data. ER, UW, and AF performed experiments. LCT, FD, HB, and SS performed quality control and processed the RNA-seq data. LCT, WRS, KR, MP, and YG conducted the bioinformatics analysis of the processed data. MS, RU, BEPW, SS, LCT, and JEG contributed to the keratinocyte RNA-seq experimental data or the analysis. ER, LCT, PH, EM, JTE, AF, JEG, and SW contributed to the biological inference of the analysis results. LCT, JEG, and SW wrote the manuscript.

      Supplementary Material

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