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. Author manuscript; available in PMC: 2011 Jul 2.
Published in final edited form as: J Invest Dermatol. 2010 Mar 11;130(7):1829–1840. doi: 10.1038/jid.2010.36

Assessment of the Psoriatic Transcriptome in a Large Sample: Additional Regulated Genes and Comparisons with In Vitro Models

Johann E Gudjonsson 1,*, Jun Ding 2,*, Andrew Johnston 1, Trilokraj Tejasvi 1, Andrew M Guzman 1, Rajan P Nair 1, John J Voorhees 1, Goncalo Abecasis 2, James T Elder 1,3
PMCID: PMC3128718  NIHMSID: NIHMS210818  PMID: 20220767

Abstract

To further elucidate molecular alterations in psoriasis, we performed a gene expression study on 58 paired lesional and uninvolved psoriatic and 64 control skin samples. Comparison of involved psoriatic (PP) and normal (NN) skin identified 1,326 differentially regulated transcripts encoding 918 unique genes (548 up- and 369 down-regulated), of which over 600 were to our knowledge unreported, including S100A7A, THRSP and ELOVL3. Strongly up-regulated genes included SERPINB4, PI3, DEFB4 and several S100 family members. Strongly down-regulated genes included Wnt-inhibitory factor-1 (WIF1), betacellulin (BTC), and CCL27. Enriched gene ontology categories included immune response, defense response, and keratinocyte differentiation. Biological processes regulating fatty acid and lipid metabolism were enriched in the down-regulated gene set. Comparison of the psoriatic transcriptome to the transcriptomes of cytokine-stimulated cultured keratinocytes (IL-17, IL-22, IL-1α, IFN-γ, TNF-α and oncostatin-M) revealed surprisingly little overlap, with the cytokine stimulated keratinocyte expression representing only 2.5%, 0.7%, 1.5%, 5.6%, 5.0% and 1.9% of the lesional psoriatic dysregulated transcriptome, respectively. This comprehensive analysis of differentially regulated transcripts in psoriasis provides additional insight into the pathogenic mechanisms involved and emphasizes the need for more complex yet tractable experimental models of psoriasis.

Keywords: psoriasis, gene expression, microarray, skin, immunology

Introduction

Psoriasis is a genetically determined chronic inflammatory disease of the skin characterized by sharply demarcated scaly red plaques commonly located on the extensor surfaces of the skin. The most characteristic feature of psoriasis is the marked hyperproliferation and altered differentiation of the epidermis. Additionally, psoriasis has complex immunologic, biochemical, vascular and neurologic alterations. Psoriasis has been shown to be immune mediated as targeted treatments against T cells (Ellis et al., 1986) or key inflammatory mediators such as TNF-α (Gottlieb et al., 2003; Leonardi et al., 2003) will lead to near complete remission of the disease. Recent genetic association studies of psoriasis support this concept but suggest that the story is more complex, with variants in genetic loci regulating barrier function (de Cid et al., 2009; Zhang et al., 2009) and antimicrobial defenses (Hollox et al., 2008) in addition to antigen presentation (Nair et al., 2006), NF-κB signaling (Nair et al., 2009), and T cell polarization (Chang et al., 2008; Nair et al., 2009) all playing a role in its pathogenesis.

Earlier studies on the functional pathways involved in psoriasis pathogenesis were limited to one or a few genes or proteins at a time. Examples include transforming growth factor (TGF)-α (Elder et al., 1989), S100A7 (Madsen et al., 1991), S100A8 and S100A9 (Kelly et al., 1989), protease inhibitor-3 (PI3/elafin/skin-derived anti-leukoproteinase[SKALP]) (Schalkwijk et al., 1990), IFN-γ (Bjerke et al., 1983) and IL-8 (CXCL8) (Christophers et al., 1989). Recently, array-based techniques that profile multiple genes at the same time have become important methods to characterize molecular alterations. The first array-based gene expression study performed on psoriasis identified 159 genes in 8 psoriasis patients that were differentially regulated between uninvolved and lesional skin (Oestreicher et al., 2001). Likewise, a similar study on 15 psoriasis patients identified a total of 177 genes that differed in expression in lesional versus normal skin ((Bowcock et al., 2001). Another study on 15 atopic dermatitis patients and 14 psoriasis patients identified up-regulation of 62 genes in psoriasis compared to atopic skin (Nomura et al., 2003). More recent array-based studies have indicated that over 1,000 genes may be differentially regulated in psoriatic lesions compared to normal skin (Haider et al., 2006; Zhou et al., 2003). Interestingly, a number of these genes are differentially expressed in lesional skin of psoriasis relative to chronic atopic dermatitis, despite comparable epidermal hyperplasia in these two settings (de Jongh et al., 2005; Nomura et al., 2003).

These studies provide a detailed but complex picture of gene expression events in lesional psoriatic skin. To aid in the interpretation of such data, gene expression maps based on cytokine stimulation of keratinocytes have recently been published (Bando et al., 2007; Banno et al., 2004; Finelt et al., 2005; Gazel et al., 2006; Haider et al., 2008b; Mee et al., 2007; Nograles et al., 2008). These genomic maps provide insights into the cellular sources of the gene expression signature and the complex positive and negative feedback signaling pathways occurring between different cell types. However, these model systems utilize monolayer keratinocyte cultures and therefore may not fully recapitulate the differentiated and multicellular environment present in normal epidermis, uninvolved psoriatic skin, and psoriatic plaques.

We have previously used the dataset presented here to explore the roles of the sonic hedgehog (Gudjonsson et al., 2009a) and Wnt5a pathways in psoriasis (Gudjonsson JE et al, submitted). We have also studied the expression of genes mapping to confirmed susceptibility loci in psoriasis (Nair et al., 2009) as well as the profile of differential gene expression comparing uninvolved psoriatic (PN) and normal (NN) skin (Gudjonsson et al., 2009b). The aims of this study were (i) to further delineate the gene expression profile of lesional psoriatic (PP) relative to PN and NN skin in a large dataset; (ii) to compare our results with previously published microarray studies of psoriasis, and (iii) to explore how well published transcript maps derived from cytokine-stimulated cultured keratinocytes represent the alterations observed in lesional psoriatic skin.

Results

Alterations in gene expression in lesional psoriatic skin

Unsupervised principal component analysis (Figure 1) of the 58 paired PP and PN and 64 NN samples revealed near complete separation of the PN and NN samples from the PP samples (Gudjonsson et al., 2009b). As previously described (Gudjonsson et al., 2009b; Zhou et al., 2003), there was substantial overlap between the PN and NN samples. These results demonstrate a distinct gene expression profile of PP skin that is markedly different from those of PN and NN skin. Based on our criteria for differentially regulated transcripts (see Materials and Methods), we identified 1,326 probes, encoding 918 unique genes, that differed in expression between PP and NN samples (549 genes up-regulated and 369 genes down-regulated) and 1,085 probes detecting 758 unique genes, between PP and PN samples (Figure 2). There was considerable overlap between the different groups of genes (Figure 3A and 3B). More genes were differentially expressed in PP vs. NN skin than in PP vs. PN skin, suggesting that PN skin has subtle changes in gene expression pattern that make it more similar to PP skin than NN skin is (Supplemental File 1). The raw microarray data of this study has been deposited in the NCBI Gene Expression Omnibus (GEO, http://www.ncbi.nlm.nih.gov/geo) and is accessible through GEO Series accession number GSE13355.

Figure 1.

Figure 1

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Hierarchical clustering of the entire data sample. This figure has been presented previously (Gudjonsson et al., 2009b)and is presented here for comparison. Unsupervised hierarchical clustering showed near complete separation of the PP (n=58) samples from the PN and NN samples, whereas there was some overlap between PN (n=58) and NN (n=64) samples (Gudjonsson et al., 2009b).

Figure 2. Heatmap of differentially regulated transcripts between PP and NN skin.

Figure 2

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Differentially regulated transcripts between PP and NN skin (n=1,326) are shown in a heatmap image. Blue indicates low expression levels whereas red indicates high expression levels. Black bar above heatmap indicates NN skin, purple bar above heatmap indicates PP skin. Note that the number and intensity of up-regulated transcripts exceeds the number and intensity of up-regulated transcripts. Clustering was done only on rows and based on row means, for column samples were grouped into NN or PP groups without any clustering.

Figure 3. Venn diagram of up- and down-regulated genes in psoriasis skin.

Figure 3

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This diagram shows comparison of the overlap between PP vs. NN and PP vs. PN and PN vs. NN genes for all up- and down-regulated genes. Not unexpectedly, there is a large overlap between the PP vs. NN and PP vs. PN datasets. There was a larger overlap of PN vs. NN with PP vs. NN (27 up-regulated genes and 52 down-regulated genes) compared to the PP vs. PN (24 up-regulted genes and 19 down-regulated genes) dataset suggesting that PN skin is more similar to PP skin.

The most higly up- and down-regulated genes

Several genes were increased more than 50-fold in PP compared to NN skin (Table 1A). These included the peptidase inhibitors serpin peptidase inhibitor, clade B, member 4 (SERPINB4) and -3 (SERPINB3), PI3, and the antimicrobial peptide human β-defensin 2 (DEFB4). Other up-regulated genes included members of the S100 family of proteins such as S100A7L1, S100A12, S100A9 and S100A7, keratin 16 (KRT16) and IL8. Only two genes showed more than 10-fold down-regulation in PP skin: Wnt-inhibitory factor -1 (WIF1) and betacellulin (BTC). Other notable down-regulated genes included thyroid hormone responsive spot 14 (THRSP), interleukin 1 family member 7 (IL1F7) and chemokine (C-C- motif) ligand 27 (CCL27) (Table 1B).

Table 1. The top 15 up and down-regulated genes in PP vs. NN skin.

Up-regulated PP vs. NN
Gene symbol Gene title Fold change FDR p-value Mean in control (log2) Mean in uninvolved (log2) Mean in lesional (log2)
SERPINB4 serpin peptidase inhibitor, clade B, member 4 377 0 4.9 5.5 13.5
DEFB4 defensin, beta 4 197 0 6.2 6.8 13.9
S100A7L1 S100 calcium binding protein A7-like 1 150 0 5.0 5.2 12.2
PI3 peptidase inhibitor 3, skin-derived (SKALP) 131 0 6.5 7 13.5
SERPINB3 serpin peptidase inhibitor, clade B, member 3 64 0 7.0 7.4 13.0
SPRR2C small proline-rich protein 2C 58 2.2E-264 4.4 4.6 10.3
AKR1B10 aldo-keto reductase family 1, member B10 58 0 5.9 6.4 11.8
S100A12 S100 calcium binding protein A12 (calgranulin C) 57 0 4.7 4.9 10.6
S100A9 S100 calcium binding protein A9 (calgranulin B) 57 0 8.7 9.3 14.5
C10orf99 chromosome 10 open reading frame 99 32 0 6.0 6.9 11.4
KYNU kynureninase (L-kynurenine hydrolase) 25 0 6.1 6.1 10.8
LCE3D cornified envelope 3D 24 0 8.9 9.7 13.5
S100A7 S100 calcium binding protein A7 (psoriasin 1) 18 3.3E-114 9.9 10.7 14.1
IL8 interleukin 8 17 1.3E-53 4.3 4.4 8.4
KRT16 keratin 16 17 5.7E-272 9.4 9.4 13.5
Down-regulated PP vs. NN
Gene symbol Gene title Fold change FDR p-value Mean in control (log2) Mean in uninvolved (log2) Mean in lesional (log2)
WIF1 WNT inhibitory factor 1 -14.0 4.5E-91 9.6 9.4 5.8
BTC betacellulin -13.9 5.6E-167 8.4 8.6 4.6
THRSP thyroid hormone responsive spot 14 -9.4 1.6E-24 11.1 10.5 7.9
IL1F7 interleukin 1 family, member 7 (zeta) -8.4 3.5E-79 9.4 9.7 6.3
CCL27 chemokine (C-C motif) ligand 27 -7.7 7.5E-76 9.9 10.1 7.0
KRT1B keratin 1B -7.6 4.9E-112 11.6 11.7 8.7
MSMB microseminoprotein, beta- -6.5 2.4E-55 7.3 7.1 4.6
ELOVL3 elongation of very long chain fatty acids -like 3 -6.5 1.4E-18 8.9 8.0 6.2
GAL galanin -6.4 1.1E-16 9.4 8.5 6.7
FABP7 fatty acid binding protein 7, brain -6.0 1.7E-19 9.5 9.1 6.9
ACSBG1 acyl-CoA synthetase bubblegum family member 1 -5.7 3.8E-19 9.2 8.5 6.6
MLSTD1 Male sterility domain containing 1 -5.2 9.8E-20 8.8 7.8 6.3
HS3ST6 heparan sulfate (glucosamine) 3-O-sulfotransferase 6 -4.2 2.9E-79 8.6 8.6 6.5
WDR72 WD repeat domain 72 -4.2 5.3E-46 8.3 8.1 6.2
SERPINA12 serpin peptidase inhibitor, member 12 -4.1 1.5E-38 11.2 11.5 9.2
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QRT-PCR confirmation of differentially regulated genes

We confirmed the up- and down-regulation of several genes in PP vs. PN and NN skin by QRT-PCR. On average, DEFB4 and S100A7L1 (S100A7A) were up-regulated more than 2,000- and 4,000-fold in PP vs NN skin, respectively, whereas PI3 was up-regulated more than 1,300-fold, S100A12 about 63-fold, SPRR2C about 15-fold, and IL8 about 150-fold (Figure 4). Likewise, we found that the genes ELOVL3 and BTC were down-regulated by 30-fold in PP vs. NN skin, whereas THRSP was down-regulated by ∼8-fold (Figure 4).

Figure 4. QRT-PCR confirmation of several of the differentially expressed genes.

Figure 4

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PI3 (SKALP), DEFB4, S100A12, SPRR2C and S100A7L1(S100A7A) were up-regulated 1,300-fold, 2,200-fold, 63-fold, 15-fold, and 4500-fold respectively in PP vs. NN skin, whereas ELOVL3, THRSP and BTC were down-regulated 30-fold, 8-fold and 36-fold respectively in PP skin. The results are shown relative to the expression of the housekeeping gene RPLP0 (36B4) and as mean + SEM, n=30. Statistical significance tested with Student's two-tailed t-test assuming equal variances and indicated by ** p<0.01 and *** p<0.001, for PP versus PN skin.

Comparison to previously published microarray studies

We compared our list of unique up- and down-regulated genes to those of Zhou et al. (Zhou et al., 2003), Oestreicher et al., (Oestreicher et al., 2001), Nomura et al (Nomura et al., 2003) and Haider et al (Haider et al., 2006) using the same criteria of significance (>2.0 fold change, p<0.05) but removing duplicate genes, hypothetical transcripts and unnamed expressed sequence tags. Of the 549 unique up-regulated genes in our data sample only 136 were present in the dataset of Zhou et al (Zhou et al., 2003) (Supplemental Figure 1A and 1B). Likewise, of the 369 down-regulated transcripts in our dataset only 57 genes were found to be present in both datasets (supplemental File 2). Notable among the replicated up-regulated genes were RNASE7 (up-regulated 3.1-fold, p=5.6E-24), KLK8 (up-regulated 2.5-fold, p=7.8E-75). Reproducibly down-regulated genes included IL17D (down 2.0-fold, p=7.1E-23), CD207 (encoding langerin, down-2.9-fold, p=1.2E-29). Of the 159 genes that Oestreicher et al. identified in PP skin (Oestreicher et al., 2001), 153 encoded unique genes. Of those, we could confirm the expression of 49 in our dataset (32%) (Supplemental Figure 1A and 1B). We also compared our datset to that of Nomura et al (Nomura et al., 2003). This study was limited to genes that were differentially expressed between atopic dermatitis skin and psoriatic skin. Of the 59 unique genes described we found 36 (61%) to be present in our up-regulated gene list (data not shown). Comparison to the results of Haider et al (Haider et al., 2006) showed even greater overlap. This analysis was limited to genes that were up-regulated in both SCC and psoriasis and were more than >2-fold up-regulated in PP vs. NN skin. Of the 102 unique genes that fulfilled these criteria, 84 (82%) were present in our up-regulated PP vs. PN and NN gene list (Supplemental file 2). Finally, we compared our differentially expressed gene lists with those from Yao et al. (Yao et al., 2008). We found that among 721 up-regulated probesets (PP vs. PN) in our list, 666 (92,4%) were present in that of Yao et al., and among 364 down-regulated transcripts (PP vs. PN), 323 (88.7%) were also present in that of Yao et al.

Gene Ontology and pathway analyses

Gene ontology (GO) analysis was focused on three major categories, biological processes, cellular compartments and molecular function. Because the gene expression profiles of PN and NN skin are so similar (Gudjonsson et al., 2009b), we compared the gene expression signature of PP skin vs. to that of NN and PN skin combined in order to increase statistical power. For the up-regulated genes the processes most significantly enriched for up-regulated transcripts in the combined comparisons of PP vs. NN and PP vs. PN skin were “immune response”, “defense response” and “response to wounding” (Figure 5). Our analysis identified 90 up-regulated genes that were annotated with immune response (Figure 5), as compared to 25 genes in the largest previous analysis of PP vs. NN skin (Zhou et al., 2003). Despite the fact that epidermal hyperproliferation and altered differentiation are the most obvious histologic features of psoriasis, the p-value obtained for “immune response” was 6 orders of magnitude more significant than that obtained for “mitotic cell cycle”, 9 orders of magnitude more significant than that obtained for “ectodermal development”, and 14 orders of magnitude more significant than that obtained for “epidermal cell differentiation”. The main cellular compartments involved included the extracellular space and the cornified envelope. Chemokine and cytokine activity predominated in the molecular function of the up-regulated transcripts. Down-regulated categories involved biological processes regulating fatty acid and lipid metabolism, organ development and blood circulation. Moreover, IPA analysis identified “dendritic cell maturation” as the canonical pathway most significantly and markedly enriched within the differentially regulated transcripts, with 25 of 165 pathway components being differentially regulated (p = 1.3×10-8). Likewise, canonical pathways involving LXR/RXR activation (p<1.0×10-6), IL-10 signaling (p<1.0×10-5), pattern recognition receptors (p<1.0×10-4), and interferon signaling (p<1.0×10-4) were highly enriched (Supplemental File 3). These results are in excellent agreement with a previous gene ontology analysis of the psoriatic transcriptome. (Zhou et al., 2003)

Figure 5. Enriched gene ontology (GO) categories for up- and down-regulated transcripts.

Figure 5

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Comparison to published cytokine stimulated keratinocyte transcriptomes

We compared the differentially regulated genes that we found in PP skin vs. PN and NN skin to previously published datasets describing cytokine-induced gene expression changes in cultured keratinocytes (Bando et al., 2007; Banno et al., 2004; Finelt et al., 2005; Mee et al., 2007; Nograles et al., 2008). These published analyses were focused on cytokines previously implicated in the pathogenesis of psoriasis, including IL-1α (Mee et al., 2007), oncostatin M (OSM) (Finelt et al., 2005), TNF-α (Banno et al., 2004), IFN-γ (Mee et al., 2007), and the Th17 cytokines; IL-22 and IL-17 (Nograles et al., 2008). For this analysis, we used the same criteria for change that we used for the comparison of PP skin vs. PN and NN skin (>2 fold change, p<0.05). Several of the cytokine-induced transcripts observed in these studies displayed considerable overlap with that of the PP transcriptome. Thus, IL-22 treatment caused up-regulation of four genes (S100A7, SERPINB4, S100P, SERPINB1) all of which were also up-regulated in the PP transcriptome (Figure 6). Likewise there was substantial overlap between IL-17 induced genes (Nograles et al., 2008) and lesional gene expression, with 69% of IL-17 induced genes also being significantly up-regulated in the PP transcriptome. Up-regulated genes included human β-defensin 2 (DEFB4), the S100 family members S100A7 and S100A12, and the chemokines CCL20, CXCL1, CXCL3 and IL8. The fold-induction of these genes in keratinocytes were commonly comparable to that observed in PP skin. Thus, IL-17 treatment of keratinocytes induced expression of human β-defensin-2 (DEFB4) (238-fold), CXCL8 (14-fold) and CCL20 (28-fold), compared to 197, 17 and 7.8-fold in PP skin respectively (Table 1). Interestingly, some of the genes were induced more highly in keratinocyte cultures compared to PP skin. Thus, IL-22 and IL-17 stimulated S100A7 expression by a factor of 458 and 189, respectively (Nograles et al., 2008), compared to 18-fold induction in PP skin, possibly because basal levels of S100A7 are very low in keratinocytes (Elder and Zhao, 2002) and the heterogenous source of RNA in psoriatic lesional skin compared to these homogenous keratinocyte cultures could dilute the keratinocyte derived RNA fraction.

Figure 6. Comparison of the psoriatic transcriptome to cytokine stimulated keratinocytes.

Figure 6

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We compared the PP vs. combined PN and NN transcriptome to published genomic maps obtained from cytokine stimulated keratinocytes using same criteria as were used for the PP transcriptome (≥ 2-fold change). This analysis was focused on cytokine previously implicated in the pathogenesis of psoriasis, such as IL-1α (Mee et al., 2007), OSM (Finelt et al., 2005), TNF-α (Banno et al., 2004), IFN-γ (Mee et al., 2007), IL-22 and IL-17 (Nograles et al., 2008). Note that there is no overlap between down-regulated genes in PP vs. NN/PN and IL-1α down-regulated genes. Several of the cytokines showed moderate overlap with the psoriatic transcriptome although this never represented more than a very small fraction of the entire PP transcriptome (maximum 53/649 = 5.6% for IFN-γ).

There was also considerable overlap between IFN-γ -induced genes (Mee et al., 2007) and PP gene expression. IFN-γ induced expression of several chemokines found to be increased in PP skin such as CXCL9 and CXCL10 but suppressed several genes involved in cell proliferation that were up-regulated in PP skin, including the cyclins A2, B1 and B2 (CCNA2, CCNB1, CCNB2 respectively) and cell division cycle genes CDC2 and CDC20 (Mee et al., 2007). These data are consistent with the observation that IFN-γ is one of the key cytokines mediating the growth-promoting effect of psoriatic T-cells (Bata-Csorgo et al., 1995), even though it has a growth-suppressive effect on cultured keratinocytes in monolayer culture (Nickoloff et al., 1984). Not surprisingly, there was also some overlap between TNF-α induced genes and PP gene expression (Banno et al., 2004). TNF-α treatment of cultured keratinocytes induced expression of a number of chemokines and cytokine receptors found to be up-regulated in PP skin, including IL-8 (CXCL8), CXCL1, CXCL2, CCL20, IL1B, IL7R and IL4R.

Although we found OSM mRNA to be elevated in PP skin (1.3-fold, p<0.0001), there was minimal overlap between up-regulated genes in PP skin and genes induced by OSM in keratinocyte monolayers (Finelt et al., 2005) (Figure 6). Interestingly, however, when epidermal constructs were used instead of keratinocyte monolayer cultures, the number of overlapping genes increased from 15 / 189 (7.9%) (Finelt et al., 2005) to 50 / 279 (17.9%) (Gazel et al., 2006).

There was a much smaller overlap between down-regulated genes in cytokine-stimulated keratinocytes and that of the PP down-regulated transcriptome. Thus, only four of the transcripts down-regulated by IFN-γ were also down-regulated in PP skin, along with only four of the TNF-α down-regulated, three of the IL-22 down-regulated, three of the OSM down-regulated, and only two of the IL-17 down-regulated genes (Figure 6). As was observed for the corresponding up-regulated genes, the number of overlapping down-regulated genes in the OSM-stimulated skin equivalent model increased from 2% (3 out of 150) to 6.5% (14 out of 216) (Gazel et al., 2006) (Supplemental file 4).

For several of the cytokines, there was an overlap between the down-regulated genes in treated keratinocytes and up-regulated genes in PP skin. Thus, for IFN-γ, out of 96 down-regulated genes, 23 were present on the PP up-regulated gene list. Similarly, for OSM-treated keratinocyte monolayers (Gazel et al., 2006), out of 150 down-regulated genes, 19 were present on the PP up-regulated gene list. Reminiscent of the effects of IFN-γ on cultured keratinocytes (Nickoloff et al., 1984), many of these genes encoded cyclins (CCNA2, CCNB1) and other components involved in cell cycle regulation such as CDC2, CDC20 and CDKN3. While there are no reports of the effects of OSM on the proliferation of monolayer keratinocytes, it has been shown to induce migration of monolayer keratinocytes and induce hyperplasia of keratinocytes in reconstituted human epidermis (Boniface et al., 2007).

Discussion

This is the largest study of global gene expression in psoriasis to date and provides a global view of the biological changes that occur in lesional skin. Many of the changes that we observed are confirmatory of previous observations ((Oestreicher et al., 2001; Yao et al., 2008; Zhou et al., 2003), but the data presented here expand these in scope and detail and provide a greater in-depth insight into the biological changes that occur in psoriatic lesions.

Several factors could be responsible for the somewhat limited overlap between our study and these other studies. The first of these is study design. In the papers of Nomura et al. (Nomura et al., 2003) the aim was to compare gene expression of psoriasis with that of atopic dermatitis whereas in Haider et al (Haider et al., 2006), the PP transcriptome was compared to that of squamous cell carcinoma. Second, the larger sample size of this study provides increased power to identify differentially-regulated Third, these studies were performed on different microarray platforms, representing overlapping but non-identical visualizations of the human transcriptome, although there is greater overlap with the more recent arrays possibly reflecting larger number of probesets that these assays have in common. Thus, several of the genes reported by Zhou et al. represented expressed sequence tags and hypothetical genes that are no longer annotated as genes on the microarray that we used. Moreover, several of the genes reported by Zhou et al. later proved to be different probes targeting the same genes, leading to overestimation of the number of genes involved. One other cause of discrepancy between datasets is batch effect (Akey et al., 2007). In comparing our dataset to that of Yao et al. (Yao et al., 2008) we noted that pairs of non-lesional/lesional skin samples were not always processed together at the same time, therefore creating a batch-effect inflating the number of differentially expressed genes. We avoided this confounding effect by running the paired uninvolved and lesional samples at the same time. Overall, most of our differentially expressed transcripts were present in the dataset of Yao et al. indicating that the data from Yao et al, included truly differentially expressed genes plus those “noisy” genes that were introduced due to the batch effect in the data.

One of the canonical pathways that was up-regulated in our dataset involved members of the interferon signaling pathway with 8 of 29 pathway components up-regulated. The interferons consist of two classes: type I, which include IFN-α and IFN- β, which have an important role in anti-viral defenses and are almost exclusively derived from plasmacytoid dendritic cells, and type II, which includes IFN-γ, which is the prototypical Th1 cytokine. Subcutaneous injection of IFN-γ has been shown to induce localized psoriasis in the uninvolved skin of psoriatic patients (Fierlbeck et al., 1990). More recently, plasmacytoid derived type I interferons where shown to have a central role in the onset of psoriatic lesions in a spontaneous xenograft model of psoriasis (Nestle et al., 2005). Consistent with previously published studies (Nestle et al., 2005) we could not detect up-regulation of the type I interferons in established chronic plaque lesions. However, the type II interferon; IFNG was 1.4-fold up-regulated (p = 2.1×10-38). The most strongly up-regulated genes belonging to the interferon signaling pathway were STAT1 (2.8-fold), MX1 (4.5-fold), IRF1 (2.0-fold), IRF9 (2.1-fold) and OAS1 (4.4-fold). Moreover, at least 40 other interferon-inducible transcripts were up-regulated at least 2-fold in PP vs. NN skin including OASL (9.9-fold), OAS2 (9.0-fold), RGS1 (7.0-fold), IFI27 (8.0-fold), CXCL10 (7.1-fold) PBEF1 (5.3-fold), IFI6 (5.0-fold) and CXCL1 (5.8-fold). These results confirm and extend earlier studies indicative of a strong interferon response signature in psoriasis (Lew et al., 2004; Yao et al., 2008; Zhou et al., 2003). Besides activating interferon signature genes, IFN-γ can influence other cytokine pathways. We have recently shown that IFN-γ produced by T-cells is a major stimulus for the production of IL-23 by macrophages or dendritic cells (Kryczek et al., 2008), which in turn stimulates the development of Th17 cells (Harrington et al., 2005; Kryczek et al., 2008).

Several lines of evidence indicate that TNF-α has also a central role in the pathogenesis of psoriasis. Treatments that block the action of TNF-α have profound therapeutic effects on psoriasis (Lowes et al., 2007) and polymorphisms of two variants that regulate TNF-α signaling were recently identified in a genome wide association study of subjects with psoriasis and point to a possible altered NF- κB signaling pathway in psoriasis (Nair et al., 2009). The predominant source of TNF-α in psoriatic skin is activated dendritic cells (Boyman et al., 2004) and while TNF-α mRNA was only increased by 1.3-fold in PP vs. NN skin, several genes known to be up-regulated by TNF-α treatment were markedly induced, including AKR1B10 (aldose reductase, 57.6-fold (Iwata et al., 1999), IL1F9 (IL-1ε, 36.3-fold (Debets et al., 2001)), MMP12 (macrophage elastase, 9.1-fold (Feinberg et al., 2000), CCL20 (MIP-3α, 7.8-fold), IL1F5 (6.6-fold (Debets et al., 2001), and CXCL9 (MIG, 6.1-fold (Rottman et al., 2001). TNF-α has recently been demonstrated to have a role in maintaining the Th17 subset of T-cells in psoriatic skin (Zaba et al., 2007) and may synergize with IFN-γ in induction of pro-inflammatory mediators from mononuclear leukocytes (Haider et al., 2008a). Thus, there is a strong synergy between TNF-α and IFN-γ signaling in the course of T-cell activation by dendritic cells, as has been found for a variety of cell types (Boehm et al., 1997).

As mentioned earlier, the newly identified T-cell subset Th17 has recently been implicated in the pathogenesis of psoriasis and other disease involving epithelial barriers (Bettelli et al., 2006; Zaba et al., 2007). Although the transcripts directing the synthesis of IL-12 and IL-23 did not reach the threshold of 2-fold change, this likely being due to the low sensitivity of microarrays for less abundantly expressed transcripts, we found highly significant changes (p<10-10) in IL12B (p40) being up 1.2-fold, and IL23A (p19) up 1.2-fold but no change in the expression for IL12A (p35). IL-17 is strongly induces the expression of several chemokines by keratinocytes including CXCL1, CXCL3, CXCL5, CXCL6, CXCL8 (IL8), CCL20 (Nograles et al., 2008) and also induces expression of antimicrobial peptides such as beta-defensin 2 (Wilson et al., 2007). Of the 47 genes that were found to be upregulated in keratinocytes by IL-17 (Nograles et al., 2008) we could confirm 36 to be up-regulated in lesional skin, although several of these had less than 2-fold change by microarray analysis. Moreover, IL-17 suppresses TNF-α -induced CCL27 production by keratinocytes (Kanda et al., 2005); in agreement with previously published data (Nomura et al., 2003) CCL27 is among the most markedly down-regulated transcripts in PP skin (Table 1). In contrast to IL-17, IL-22 is a relatively weak inducer of chemokine expression in keratinocytes but has more marked effect on the differentiation and proliferation of stratified epithelium and expression of antimicrobial proteins such the S100 family of proteins (Sa et al., 2007; Wolk et al., 2006). Of the 21 unique genes induced or suppressed in keratinocytes by IL-22 (Nograles et al., 2008), 17 were found to be similarly changed in PP skin. The high expression of antimicrobial peptides is characteristic for psoriasis, this is in contrast to atopic dermatitis, which despite comparable epidermal proliferation, is not characterized by a similar overexpression of these genes (Buchau and Gallo, 2007; de Jongh et al., 2005). Many of the most highly up-regulated transcripts we have identified in psoriasis encode antimicrobial peptides, including DEFB4 (197-fold), S100A7 (18-fold), S100A8 (12-fold), S100A9 (57-fold), S100A12 (57-fold), S100A7L1 (also known as S100A15 and is now officially designated S100A7A) (150-fold), and LCN2 (17-fold) (Table 1).

Despite being limited by the lack of many of the cellular players in psoriasis, including inflammatory cells, fibroblasts and endothelial cells, keratinocytes cultures have been used as a models to gain insights into cytokine driven gene expression changes in psoriatic skin either using keratinocytes in monolayer or as epidermal constructs. These cytokines include IL-1 (Mee et al., 2006; Yano et al., 2008), OSM (Boniface et al., 2007; Finelt et al., 2005), TNF-α (Banno et al., 2004, 2005) and IFN-γ (Banno et al., 2003; Mee et al., 2006; Mee et al., 2007). While treatment with either IL-1 or OSM upregulated several of the most strongly up-regulated transcripts observed in PP skin, including SERPINB4, DEFB4, IL1F9, PI3, S100A7, S100A8, S100A9, and S100A12, other strongly up-regulated transcripts, such as CXCL9 and CXCL10 were not; rather, these transcripts were strongly induced by IFN-γ. Conversely, several transcripts that were strongly induced in culture were not strongly up-regulated in psoriasis. Although some of this discrepancy is influenced by the heterogenous mixture of cells involved in lesional psoriatic skin compared to the homogenous keratinocyte cultures, it is likely that the psoriatic transcriptome reflects exposure of the epidermis to a medley of multiple cytokines, producing a response that can be partially but not completely mimicked by any single cytokine alone as was recently shown in a novel skin equivalent system (Tjabringa et al., 2008). Likewise, it appears that the model system used has important implications for the gene expression pattern observed. This is clearly demonstrated by the OSM studies (Finelt et al., 2005; Gazel et al., 2006) where the overlap between gene-expression signature and PP skin increased from 7.9% to 17.9% by using reconstituted epidermal constructs instead of semi-confluent keratinocyte cultures. Finally, one study examined a panel of 33 cytokines singly and in various combinations, and found that IL-1α and β, IL-6, IL-17, IL-20, IL-22, IL-24, and TNF-α could provoke S100A7 and DEFB4 responses similar to that elicited by OSM. They also observed marked synergy between IL-17, TNF-α, and OSM as the major inducers of S100A7 and DEFB4 (Boniface et al., 2007).

Many of the most highly up-regulated genes that we encountered reside in the EDC located on chromosomal band 1q21. In addition to the S100 genes involved in the innate immune response, these include the SPRR, and LCE gene families whose members play important roles keratinocyte terminal differentiation (Eckert et al., 2004; Gibbs et al., 1993; Jackson et al., 2005). These gene families are structurally distinct and rapidly evolving (Jackson et al., 2005; Ravasi et al., 2004), suggesting that this distinctive chromosomal region is under some form of long-range epigenetic regulation (Segre, 2006). Other clustered and highly up-regulated genes encode the serine peptidase inhibitors SERPINB3 (up 64-fold), SERPINB4 (up 377-fold) and SERPINB13 (up 7.5-fold) on chromosome 18q21.3, and the kallikrein serine proteases KLK6 (up 9.2-fold) and KLK13 (up 9.8-fold) on chromosome19q13. The mechanism(s) whereby these clustered genes are coordinately up-regulated in psoriasis remain to be determined.

Psoriasis is characterized by a markedly altered balance of proteolytic and anti-proteolytic activities involved in keratinocyte turnover (Magert et al., 2005) consistent with the observation that catalytic activity was one of the most highly upregulated molecular functions up-regulated in PP skin (Figure 5). In addition to the clustered proteases and inhibitors described above, the PI3 gene (up 131-fold) encoding protease inhibitor-3 (SKALP/elafin) is markedly over-expressed in psoriasis (Alkemade et al., 1994). Several of these proteases were stimulated by cytokines in keratinocyte cultures, particularly PI3 and members of the serine protease inhibitor, clade B, family (SERPINB). Thus, IL-1α IL-17 and IL-22 induced expression of SERPINB4 (Bando et al., 2007; Nograles et al., 2008) in keratinocytes. OSM, IFN- γ and TNF-α had increased expression of SERPINB1 (Banno et al., 2004; Finelt et al., 2005; Mee et al., 2007), IL-1α induced expression ofSERPINB3 and PI3 (Bando et al., 2007) whereas TNF-α induced SERPINB8 expression (Banno et al., 2004). Interestingly, OSM stimulation of reconstituted epidermal constructs greatly increased the number of overlapping proteases and protease inhibitors with that of PP skin (KLK13, PI3, SERPINB1, SERPINB4, SERPINA1 and SERPINA3) (Gazel et al., 2006). In all instances the expression observed in keratinocytes or reconstituted epidermis was lower than that observed in PP skin. This indicates that expression of proteases and protease inhibitors are rather a function of stratified epithelia in contrast to keratinocyte monolayer cultures. Furthermore, the dramatic over-expression of these proteases and anti-proteases demonstrates that control of the proteolytic environment is a crucial element of epidermal homeostasis and barrier function (Meyer-Hoffert, 2009).

Interestingly we observe large alterations in genes involved in metabolism, particularly lipid and fatty acid metabolism (Figure 5), and amino acid metabolism (Supplemental File 3). Kynureninase (KYNU) is one of the genes that was amongst the most highly up-regulated genes in psoriatic skin in our dataset (table 1). Upregulation of KYNU as been reported previously in psoriasis (Ito et al., 2004; Nomura et al., 2003) as well as atopic dermatitis (Ito et al., 2004) The product of this gene is involved in tryptophan metabolism and 11 other genes involved in the tryptophan pathway were found to be differentially regulated in psoriatic skin (Supplemental File 3). Importantly, activity of this pathway is induced by IFN-γ (Ito et al., 2004; Taylor and Feng, 1991). The exact role of this pathway in psoriasis is unknown but upregulation of trypotophan catabolism has been shown to confer antibacterial effector functions on multiple cell types including epithelial cells (Daubener and MacKenzie, 1999). The changes we observe for genes involved in lipid and fatty acid metabolism are similar to changes that we have previously reported in uninvolved and lesional psoriatic skin and suggests an defect involving the lipid-barrier of the epidermis of psoriatic skin (Gudjonsson et al., 2009b).

Taken together, these results are broadly consistent with the model for the development and maintenance of psoriatic lesions that has emerged in recent years. Thus complex two-way interactions between activated dendritic cells, T- cells and keratinocytes, are largely mediated by cytokines and chemokines, and punctuated by periodic bursts of neutrophilic infiltration of the epidermis (Lowes et al., 2007). Interestingly, several of the most highly up-regulated genes in PP skin drive neutrophil infiltrations, including IL8 (CXCL8) (up 17 fold vs. NN) and CXCL1 (up 5.8-fold vs. NN). However, neutrophils appear to be dispensable for the development of psoriasis in a transgenic mouse model (Stratis et al., 2006), focusing attention on mononuclear cells.

We observed fewer down-regulated than up-regulated transcripts in PP skin, compared to either PN or NN skin. Of the down-regulated transcripts observed in PP skin (Table 1) WIF1, encoding Wnt inhibitory factor-1, and BTC, encoding betacellulin, were the most prominent. WIF-1 is a secreted protein that binds to Wnt proteins and inhibits their activities (Hsieh et al., 1999). Taken together with the increased expression of the non-canonical wnt-member; WNT5A, that we and others (Reischl et al., 2007; Zhou et al., 2003) have observed, we have proposed that the non-canonical Wnt pathway may be activated in psoriasis (Gudjonsson et al., submitted). Betacellulin is one of several epidermal growth factor (EGF) receptor ligands expressed in skin, along with amphiregulin, heparin-binding EGF, TGF–α, and epiregulin. In contrast to the other EGF family ligands, many of which have been reported to be up-regulated (Elder et al., 1989; Shirakata et al., 2007) betacellulin is down-regulated in psoriasis, as shown by immunohistochemistry (Piepkorn, 1996; Piepkorn et al., 2003) and by a previous microarray analysis (Zhou et al., 2003). These results suggest that betacellulin may have a role in maintenance of the differentiated phenotype of the epidermis (Piepkorn et al., 2003). In addition to CCL27 discussed earlier, interesting transcripts that were markedly down-regulated in PP vs. NN skin included THRSP, encoding a nuclear protein involved in fatty acid synthesis (Cunningham et al., 1998), GAL, encoding galanin, a vasoactive peptide that mediates vasoconstriction and inhibition of blood flow (Schmidhuber et al., 2007), IL1F7 which has been shown to be expressed in fully differentiated keratinocytes in the stratum granulosum of the epidermis (Busfield et al., 2000) and MSH5, thought to participate in mitotic DNA repair (Her et al., 2007). There was very small overlap between genes down-regulated by individual cytokines and those down-regulated in PP skin. Interestingly, this overlap was larger for the only study published so far on cytokine stimulated reconstituted epidermis (Gazel et al., 2006). In that study one of the transcripts that was downregulated encoded for betacellulin (BTC), which is a member of the EGF ligand family and one of the most strongly down-regulated transcripts in PP skin, but its expression is undetectable in keratinocyte monolayers (Johnston, unpublished observation). Thus, apart from the more complex situation in PP skin where various combinations and two-way interactions of cytokines and activated immune cells may affect the gene expression, this indicates that lack of stratification may be one of the reasons for the small overlap observed between down-regulated genes in PP skin and that of cytokine stimulated keratinocyte monolayers. Finally, another explanation for this discrepancy may relate to differences in epidermal and keratinocyte responses between psoriasis and healthy controls that have been proposed to be genetically determined (Zeeuwen et al., 2008).

In conclusion, this is the most comprehensive analysis of differentially regulated transcripts in psoriasis and provides a global view of the psoriatic transcriptome. This study identified group of genes that are dysregulated in lesional psoriatic skin, large proportion of which have not been described before. Our comparison of the PP transcriptional genome and the limited overlap with genomic maps obtained from previously published cytokine stimulated keratinocytes using monolayer systems indicates the limitations of using this approach and suggests that greater insights into psoriasis pathogenesis and the effect of individual key cytokines might be obtainable by using reconstituted epidermal constructs or other more elaborate tissue models. Overall, these data provides novel insights into the pathogenic mechanisms involved in psoriasis and more accurately defines the biochemical changes and pathways involved.

Materials and Methods

Subjects

The criterion for entry of a case was the presence of at least one well demarcated, erythematous, scaly psoriatic plaque that was not limited to the scalp. In those instances where there was only a single psoriatic plaque, the case was only considered if the plaque occupied more than 1% of total body surface area. We enrolled 58 patients with psoriasis and 64 normal healthy controls in the study. This study was approved by the Institutional Review Board of the University of Michigan Medical School and was conducted according to the Declaration of Helsinki Principles. Informed written consent was obtained from all study subjects. Study subjects did not use any systemic anti-psoriatic treatments for 2 weeks prior or topical anti-psoriatic treatments for 1 week prior to biopsy. Two biopsies were taken under local anesthesia from each psoriatic subject; one 6 mm punch biopsy was obtained from PP skin and the other from PN skin sampled at least 10cm away from any active plaque. One or two biopsies were obtained from the normal skin of healthy controls (NN skin). Gender was balanced in both case and control cohorts. Mean age of controls was 41.1 years (range 18-75) whereas mean age of patients was 48.5 years (range 21-69).

RNA processing and microarray hybridization

After removal from the skin, biopsies were snap-frozen in liquid nitrogen and stored at -80°C until use. Biopsies were pulverized with a hammer while still frozen, and total RNA isolation was performed using a commercial kit (RNeasy, Qiagen, Chatsworth, CA), employing glass beads (Biospec, Bartleville, OK) for homogenization. RNA quantity and quality was measured on Agilent 2100 Bioanalyzer (Agilent Technologies, Palo Alto, CA). Only samples yielding intact 18S and 28S ribosomal RNA profiles were used. cDNA and in vitro transcription for probe biotinylation were performed on 5 μg of total RNA according to the manufacturer's protocols (Affymetrix, Foster City, CA). Samples were run on HU133 Plus 2.0 arrays to query expression of >54,000 probes.

Quantitiative reverse transcription and PCR (QRT-PCR)

The reverse transcription reaction was performed on 0.15 μg RNA template and cDNA was synthesized using the High Capacity cDNA Reverse Transcription Kit per manufacturer's protocols (Applied Biosystems Inc, Foster City, CA). Primers for the genes IL8, DEFB4, S100A7A, S100A12, PI3, THRSP, ELOVL3, and BTC were obtained from Applied Biosystems. Results were normalized to the expression of the housekeeping gene RPLP0/36B4, encoding ribosomal protein, large, P0 (Minner and Poumay, 2009). QRT-PCR was carried out using an Applied Biosystems 7900HT Fast Real Time PCR System. Fold changes were calculated comparing normal vs. lesional psoriatic skin.

Microarray Data Analysis and Statistics

The Robust Multichip Average (RMA) method (Irizarry et al., 2003) was used to process the raw data from 180 microarrays. The data were adjusted to account for gender and batch effects. Specifically, we used a linear regression model to estimate gender and batch effects and then subtracted them from the RMA expression values to obtain the adjusted data. Hierarchical clustering (using a “complete” agglomeration method) and principal components analysis (PCA) were performed on the adjusted expression data using the publicly available software R (www.r-project.org). Gene expression was contrasted between PP vs. PN (by using one-sample t-tests on the difference between paired PP, PN samples) or PN vs. NN (by using two-sample t-tests) based on the following criteria: >= 2.0-fold change in the means of expression in two groups and FDR p-value <= 0.05. Transcripts with small variation across samples were not filtered out. But when the estimated variance for a gene in the t-test is less than the median variance for all genes, we used the median variance in the t-test. By doing that, we prevented genes from being designated differentially expressed if they had very small variation. Gene Ontology (GO) category enrichment analysis was performed using DAVID (Database for Annotation, Visualization and Integrated Discovery, http://david.abcc.ncifcrf.gov/, Bethesda, MD, USA). For this exploratory analysis, p = 0.001 was chosen as a stringent significance criterion and p = 0.05 as a relaxed significance criterion. QRT-PCR data was tested for significance using Student's two-tailed t-test assuming equal variances and p-values ≤ 0.05 were considered to be significant.

Ingenuity Pathway Analysis

Ingenuity Pathway Analysis (IPA) software package (Ingenuity Systems, Redwood City, CA) was used to analyze differentially regulated transcripts. For network and pathway generation, a data set containing gene identifiers and corresponding expression values was uploaded into the application. Each gene identifier was mapped to its corresponding gene object in the Ingenuity pathways knowledge base.

Supplementary Material

Supplementary Data

Acknowledgments

The authors express their appreciation for all the research subjects who participated in this study and the assistance of Lynda Hodges and Anna Pero. The authors thank Philip Stuart for technical assistance. Dr. Anne Bowcock for providing gene lists referenced in Zhou et al., Physiological Genomics 13:69-78, 2003. This work was supported by grants to J.T.E. (National Institutes of Arthritis, Musculoskeletal and Skin Diseases, R01-AR054966), J.E.G (American Skin Association, Dermatology Foundation).

Abbreviations

DAVID

Database for Annotation, Visualization and Integrated Discovery

FDR

False, Discovery Rate

EDC

epidermal differentiation complex

EGF

Epidermal Growth Factor

GO

gene ontology

IL

Interleukin

IPA

Ingenuity Pathway Analysis

LCE

Late cornified envelope

OSM

Oncostatin M

PP

Psoriatic plaque

PN

Psoriatic normal

NN

Non-psoriatic normal

SERPINB

serine protease inhibitory, clade B

SPRR(P)

small proline-rich (proteins)

TNF

Tumor necrosis factor

Footnotes

Conflict of Interest: The authors have no conflict of interest.

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