Genome-Wide Expression Analysis Suggests Unique Disease-Promoting and Disease-Preventing Signatures in Pemphigus Vulgaris

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ORIGINAL ARTICLE Genome-wide expression analysis suggests unique disease-promoting and disease-preventing signatures in Pemphigus vulgaris

R Dey-Rao, K Seiffert-Sinha and AA Sinha

To evaluate pathogenetic mechanisms underlying disease development and progression in the autoimmune skin disease Pemphigus vulgaris (PV), we examined global peripheral blood gene expression in patients and healthy controls. Our goals were to: (1) assign blood gene expression signatures to patients and controls; (2) identify differentially expressed genes (DEGs) and investigate functional pathways associated with these signatures; and (3) evaluate the distribution of DEGs across the genome to identify transcriptional ‘hot spots’. Unbiased hierarchical clustering clearly separated patients from human leukocyte antigen (HLA)-matched controls (MCRs; ‘disease’ signature), and active from remittent patients (‘activity’ signature). DEGs associated with these signatures are involved in immune response, cytoskeletal reorganization, mitogen-activated protein kinase (MAPK) signaling, oxidation-reduction and apoptosis. We further found that MCRs carrying the PV-associated HLA risk alleles cluster distinctly from unmatched controls (UMCR) revealing an HLA-associated ‘control’ signature. A subset of DEGs within the ‘control’ signature overlap with the ‘disease’ signature, but are inversely regulated in MCR when compared with either PV patients or UMCR, suggesting the existence of a ‘protection’ signature in healthy individuals carrying the PV HLA genetic risk elements. Finally, we identiﬁed 19 transcriptional ‘hot spots’ across the signatures, which may guide future studies aimed at pinpointing disease risk genes.

Genes and Immunity (2013) 14, 487–499; doi:10.1038/gene.2013.44; published online 29 August 2013 Keywords: pemphigus vulgaris; gene expression; DNA microarray; HLA; disease signatures; autoimmune disease

INTRODUCTION subjects; (2) identify differentially expressed genes (DEGs) and Pemphigus vulgaris (PV) is a prototypical organ-specific, poten- investigate functional pathways and processes associated with tially fatal autoimmune disease of the skin, with an incidence of an patient and control signatures; and (3) evaluate the distribution of estimated 0.076–5 cases in 100 000 person-years1 that is DEGs across the genome to identify chromosomal regions characterized by mucocutaneous blistering. Tissue damage harboring transcriptional ‘hot spots’ that may be useful for is mediated by autoantibodies that target self-antigens, primarily locating disease susceptibility loci. desmoglein 3 and 1, that are expressed at the surface of Our data show that unsupervised hierarchical clustering keratinocytes, resulting in acantholysis (loss of cell–cell adhesion) achieved distinct separation of patient and control groups, by mechanisms not fully understood.2–4 Although there is a producing three distinct expression patterns characterizing (1) general consensus that the disease is multifactorial, there is ‘disease’ (PV patients vs genetically related HLA-MCRs), (2) a major gap in our knowledge regarding how genetic and ‘activity’ (active vs remittent PV patients) and (3) ‘control’ (HLA- environmental elements alter gene expression and promote matched vs HLA-UMCRs) signatures. Functional annotation of disease development. Despite the very strong linkage of PV to DEGs within these signatures implicated immune pathways, human leukocyte antigen (HLA) DRB1*0402 and DQB1*0503, cytoskeletal reorganization, apoptosis and MAPK signaling path- additional questions remain, such as why the vast majority of ways in the pathogenesis of PV. Significantly, we uncovered the individuals who carry the PV-associated HLA susceptibility alleles presence of a subset of PV-related DEGs within the PV HLA- do not develop the disease. Furthermore, the molecular related ‘control’ signature that overlapped with DEGs linked to mechanisms driving the unpredictable transitions from periods the ‘disease’ signature, but that are inversely regulated (pre- of disease activity to remission and back are not known. dominantly downregulated) in MCRs when compared with either Over the past several years, global genome-wide expression PV patients or UMCRs. We propose that this subset of genes profiling has been useful to advance our understanding of disease constitute a ‘protection’ signature in genetically susceptible mechanisms and genetic risk in a number of autoimmune control individuals who have not yet developed the disease, diseases.5–9 Although target damage in PV is within the skin, it which serve to hold disease development in check. Analysis of the is clear that patients exhibit systemic alterations in their peripheral distribution of DEGs associated with the four signatures described immune system.4,10 Thus, we undertook this study with the above identified 19 transcriptional ‘hot spots’ that highlight objectives to: (1) assign blood gene expression signatures to PV chromosomal regions, which may facilitate the search for PV patients and HLA-matched (MCR) and -unmatched control (UMCR) susceptibility genes.

Department of Dermatology, University at Buffalo, Buffalo, NY, USA. Correspondence: Dr AA Sinha, Department of Dermatology, University at Buffalo, 6082 Clinical and Translational Research Center, 875 Ellicott Street, Buffalo, NY 14203, USA. E-mail: [email protected] Received 26 March 2013; revised 2 July 2013; accepted 19 July 2013; published online 29 August 2013 Genome-wide expression analysis in Pemphigus vulgaris R Dey-Rao et al 488 RESULTS samples. Briefly, the most variably expressed genes across the Unsupervised hierarchical clustering and principal components arrays were selected for hierarchical clustering. This unbiased analysis (PCA) achieved distinct classification of patient and method revealed that: (1) PV patients, regardless of disease control groups activity or therapy status, separated away from the genetically We performed unsupervised hierarchical clustering on log2 related HLA-MCR subjects (Figure 1a), producing a ‘disease’ transformed, background corrected and normalized expression signature, driven by 1203 most variable probes. The 21 PV values from all patient (n ¼ 21; 13 PV-active (PV-A), 8 PV-remittent patients were either off (OFF), on minimal (MIN) or more than (PV-R)) and control (n ¼ 10; 4 HLA-MCRs and 6 HLA-UMCRs) minimal (MORE MIN) therapy, as defined by consensus

Figure 1. Unsupervised hierarchical clustering separates PV patients and healthy controls and segregates them into three distinct signatures. (a–c) Dendograms of the three signatures: (a) PV patients, regardless of disease activity and state of therapy separate from genetically matched controls (MCRs; ‘disease’ signature); (b) active PV patients separate from remittent patients (‘activity’ signature); and (c) HLA-MCRs cluster away from HLA-UMCRs (‘control’ signature). (d–f) Principle components analysis displays clear spatial separation (expression level clustering) of the three assigned signatures: (d) ‘disease’ signature; (e) ‘activity’ signature; and (e) ‘control’ signature. In the three-dimensional plots, the three principle components PC#1, #2 and #3 of all samples and their respective variations are expressed on the x, y and z axis. The total percentage of PCA mapping variability is 42.1% for d, 74.4% for e and 59.5% for f. Each data point represents a sample. The ellipsoids highlight portioning of the different samples. Active PV (PV-A ¼ blue), remittent PV (PV-R ¼ green), HLA-MCR ( ¼ red), HLA-UMCR ( ¼ purple). Therapy status: no (OFF ¼ pink), minimum (MIN ¼ light blue) and more than minimum (MORE MIN’ ¼ yellow), as deﬁned in ‘Materials and methods’.

Genes and Immunity (2013) 487 – 499 & 2013 Macmillan Publishers Limited Genome-wide expression analysis in Pemphigus vulgaris R Dey-Rao et al 489 guidelines11 and in the ‘Materials and methods’ section. transformed normalized expression indices for 454 000 tran- Importantly, patients did not cluster according to therapeutic scripts of all samples in the three signatures: (1) ‘disease’–all groupings (Figure 1a), indicating that treatment did not drive the patients and HLA-MCRs; (2) ‘activity’–active PV and remittent PV clustering. (2) PV patients with active disease as defined by and (3) ‘control’–HLA-matched and -UMCRs. In this analysis, all PV consensus guidelines11 and in the Materials and methods section patients, regardless of disease activity, clearly separate away from (see below) were distinct from the remittent patients (Figure 1b), MCRs (Figure 1d), whereas active and remittent patients show the delineating a disease ‘activity’ signature of 1572 most variable least separation (Figures 1d and e). HLA-related MCRs were the probes. For this analysis, we narrowed our patient selection to 3 most significantly clustered away from the UMCR individuals PV-A and 3 PV-R patients that were off therapy (OFF) to eliminate (Figure 1f). There were no distinctive outliers. any possible effects of on-going therapy on gene expression Taken together, our data indicate that unbiased hierarchical related to lesional activity. (3) MCR separated from UMCR clustering and principle components analysis can assign expres- (Figure 1c) revealing a PV HLA-related ‘control’ signature driven sion signatures associated with PV based on: the absence and by 1915 most variable probes. Some of the top 10 GeneGO presence of disease in genetically susceptible individuals (‘disease’ pathways and processes associated with the up-and down- signature), variable disease phases (‘activity’ signature) and HLA regulated genes driving the difference in both the ‘disease’ and association (‘control’ signature). In addition, we clearly demon- ‘activity’ signatures by unbiased hierarchical clustering were strate that the transcriptional grouping of patients is driven by immune and inflammatory response (involving interleukin-4 (IL- disease state, and not by therapy status or gender. 4), IL-13 and IL-17), cytokine production, wound healing, oxidation-reduction, apoptosis and leukocyte activation, among others (Table 1). The IL-4 pathway has been shown to be intricately The ‘disease’ signature involved in PV pathogenesis,12 whereas the IL-17 signaling DEGs were generated by comparing the groups that produced the pathway has been described relatively recently, and shown to ‘disease’ signature by the unbiased approach (see above). have a critical role in inflammation and pathogenesis of multiple Hierarchical clustering of the trimmed 902 DEGs with 706 up autoimmune diseases.13,14 Unique to the ‘activity’ signature was and 196 downregulated (Supplementary Table 1) separated the the enrichment of genes involved in the antiviral interferon- samples into their corresponding phenotype (data not shown). related immune response. These processes and pathways are The top 10 up- and downregulated genes in the list are shown in consistent with the known or suspected pathophysiological Figure 2a. For the full list of DEGs from the ‘disease’ signature see changes occurring in PV.4,10,15–18 Most of the top enriched Supplementary Table 2. GeneGO pathways and processes were also shared in the PV Functional annotation and pathway analysis of the DEGs within HLA-related ‘control’ signature with the notable exceptions of the ‘disease’ signature revealed a significant enrichment of oxidation-reduction and apoptosis (Table 1). We note that one dysregulated pathways and processes involving regulation of actin clinically active patient (PV 201) did not cluster with the other cytoskeleton, regulation of stress-activated protein kinases in the active patients (Figure 1a). On review of the clinical intake record, MAPK signaling pathway, clathrin-coated vesicle transport cycle, this patient had an atopic diathesis with multiple allergies to dust, apoptosis, G-protein signaling pathways including p38MAPK, c-Jun pollen and mold as well as atopic dermatitis, which might be a N-Terminal Kinase (JNK) and RhoA showing most genes upregulated reason for the grouping. in PV patients as opposed to MCR (Figure 2b). We found a total of 67 Next, to identify outliers and evaluate whether significant batch DEGs involved with apoptosis and cell death (mostly upregulated) in effects could be observed, PCA was performed on Log2 PV patients vs MCRs; Apoptotic pathways have been implicated in

Table 1. Potentially disease relevant GeneGo pathways and processes (within the top 10 signiﬁcant pathways) associated with the up and downregulated genes driving the differences in the ‘disease’, ‘activity’, and ‘control’ signatures by unsupervised hierarchical clustering

Enriched GeneGo pathways ‘Disease’ signaturea ‘Activity’ signaturea ‘Control’ signaturea and processes shared

Immune response IL-17 signaling pathways IL-1 signaling pathway IL-1 signaling pathway IL-13 signaling via JAK-STAT Oncostatin M signaling via MAPK HMGB1/TLR signaling pathway in mouse cells IL-17 signaling pathways Antiviral actions of interferons Cytokine production Cytokine production by Th17 cells in Immune response_Th17-derived Cytokine production by Th17 cystic fibrosis cytokines cells in cystic fibrosis Wounding Wound healing Response to wounding Response to wounding Oxidation reduction Response to oxidative stress Oxidation reduction — Apoptosis Anti-apoptosis mediated by external Apoptosis and survival caspase — signals via NF-kB cascade Anti-apoptosis mediated by external signals via MAPK and JAK/STAT Inflammation Immune response_Th17-derived IL-4 signaling IL-4 signaling cytokines IL-10 anti-inflammatory response IL-10 anti-inflammatory response IL-4 signaling Inflammasome in inflammatory response Leukocyte Activation Cell adhesion_Platelet–endothelium– Cell adhesion_Leucocyte Cell adhesion_Leucocyte leucocyte interactions chemotaxis chemotaxis Regulation of leukocyte activation Cell adhesion_Platelet– Cell adhesion_Platelet- Cell adhesion_Leucocyte chemotaxis endothelium–leucocyte endothelium-leucocyte interactions interactions Abbreviations: IL, interleukin; MAPK, mitogen-activated protein kinase; NF-kB, nuclear factor kB. aSignatures revealed by unsupervised hierarchical clustering

& 2013 Macmillan Publishers Limited Genes and Immunity (2013) 487 – 499 Genome-wide expression analysis in Pemphigus vulgaris R Dey-Rao et al 490

Figure 2. Functional annotation and pathway analysis in the ‘disease ‘signature. We compared gene expression proﬁles from 21 PV patients (PV-A±PV-R) vs 4 matched controls (MCRs), generating a trimmed list of 902 DEGs (706 up and 196 downregulated). (a) Top 10 up and downregulated genes. (b) Functional annotation and pathways analysis reveal signiﬁcant enrichments in several patho-physiologic pathways and processes central to PV, with most transcripts upregulated in PV patients. Bars indicate fold up and downregulation of genes. A ‘positive’ FC indicates an upregulation and a ‘negative’ FC indicates a downregulation of genes in PV patients (active and remittent) as compared with genetically related HLA-MCR individuals.

the induction of acantholysis in a number of studies.15,16,19,20 Forty- therapy (OFF) for further analysis. Hierarchical clustering of 252 two protein kinases and nine protein phosphatases were DEGs generated from this comparison separated the six samples dysregulated. Many of these pathways and specific DEGs support into their corresponding phenotype (data not shown) with 203 up documented events in PV pathogenesis.15 and 49 downregulated transcripts in active vs remittent patients Of note, we observe a 2.47-fold upregulation in PV patients of (Supplementary Table 1). The top 10 DEGs are shown in Figure 3a. ATP2C1 (calcium-transporting ATPAse type 2C member 1), which See Supplementary Table 3 for the full DEG list. has been associated with Haley–Haley disease21 and PV.22 Both More than 30 DEGs, mostly upregulated in active vs remittent diseases are characterized by a loss of cell–cell adhesion because patients, are enriched for gene ontology pathways and processes of desmosomal dysfunction, which may be linked to the altered related to immunity (innate and adaptive immune response) such intracellular calcium metabolism associated with the gene.22,23 as the nucleotide-binding oligomerization domain-like receptor Also, ADAM9 (fold change (FC) ¼ 2.2), a membrane-anchored signaling pathway along with cytokine production and interaction, metalloprotease related to disintegrins has been shown to inflammatory pathways, apoptosis, leukocyte and lymphocyte mediate proteolysis of the E-cadherin cell adhesion molecules activation, and are consistent with ‘activity’-associated functional and DSG1,24,25 processes central to the development of non- pathways and processes revealed by unsupervised clustering mucosal blisters in PV.26 analysis. Activated immune response was the most highly We observed additional enriched pathways and processes such enriched process (Figure 3b), followed by oxidation reduction as neurotrophin signaling (IRS2, BRAF, FOXO3, PTPN11, IRAK3, with 19 DEGS (ME2, SORD, FADS1, NCF4, PYROXD1, SCD, UGDH, NRAS, BDNF, MAP3K5, MAP3K1, GAB1, RIPK2, MAPK9, FOXO3B and PDIA5, ALDH3B1, NDUFV3, SCCPDH, NPHP3, CYP4A11, PRUNE2, FRS2), membrane fusion (SNAP29, PLDN, GNAI3, RABIF, NAPG, TP53I3, DHFR, PLOD1, H6PD and PNPO). Upregulation of pathways BNIP1, RABEP1, VTI1B, LRMP, VAMP3, SNAP23 and GCA) and and processes such as cytokine–cytokine receptor interaction (10 sphingolipid metabolic process (ACER3, SGMS2, GLA, COL4A3BP, DEGs), inflammation pathway involving Jak-signal transducer and PPP2CA, SGMS1, NSMAF, CLN8 and SFTPB) that have not activator of transcription (STAT) signaling (7 DEGs) and apoptosis previously been associated with disease. (5 DEGs) was observed. In addition, we found an enrichment of the Ras-related G-protein, RACI signaling pathway, with three The ‘activity’ signature DEGs (C3orf10, PAK1 and NCF2). Most DEGs in all the enriched In order to evaluate transcriptional changes directly associated pathways and processes were upregulated in active patients when with different phases of clinical activity, we next eliminated compared with patients in remission (Figure 3b). control subjects from the comparison. Although unsupervised We also note significant enrichment of six DEGs (IRAK1, clustering showed that the therapeutic regimen did not affect SLC11A1, NOD2, CARD9, SCARB1 and MGST1) associated with how patients clustered (see above and Figure 1a), to avoid any the GO biological process; ‘response to molecule of bacterial therapeutic bias, we included only those six patients, three active origin’ that is of interest because of the recent association of host (PV-A) and three remittent (PV-R), which were completely off gene–microbiota interactions with blistering disease of the skin.27

Genes and Immunity (2013) 487 – 499 & 2013 Macmillan Publishers Limited Genome-wide expression analysis in Pemphigus vulgaris R Dey-Rao et al 491

Figure 3. Functional annotation and pathway analysis of the ‘activity’ signature. We compared gene expression profiles from three active (PV- A) vs three remittent (PV-R) patients who were all off therapy, generating a trimmed list of 252 DEGs (203 up and 49 downregulated). (a)Top 10 up and downregulated genes. (b) Functional annotation and pathways analysis reveal significant enrichments in pathways and processes central to the disease related to immune and inflammatory response, with most transcripts upregulated in active patients. Bars indicate fold up- and downregulation of genes. A ‘positive’ FC indicates an upregulation and a ‘negative’ FC indicates a downregulation of genes in PV-A patients as compared with PV-R patients.

The PV HLA-associated ‘control’ signature was regulated in the opposite direction in HLA-MCRs when PV exhibits one of the strongest known HLA disease associations, compared with either UMCR or PV patients. PCA of the 155 genes, where 495% PV patients type as HLA DRB1*0402 and DQB1*0503 with spatial separation of all the variations in expression values of positive.4,10 However, a large percentage of healthy individuals who the 31 samples shows clustering of MCR away from all PV patients carry these two alleles do not develop disease.10 To uncover (active and remittent) and UMCR samples (Figure 5b). Hierarchical differences in transcriptional activity in healthy HLA-matched clustering and heat map of the expression values of these 155 individuals (MCR, carrying the PV-associated DRB1*0402 and/or genes in all 31 samples (13 PV-A, 8 PV-R, 6 UMCR and 4 MCR) DQB1*0503 alleles) from healthy HLA-unmatched subjects (UMCR, clearly follows the same trend in regulation, with 132 genes being carrying neither DRB1*0402 nor DQB1*0503), we eliminated all downregulated and 23 being upregulated in MCR when compared diseased subjects from the next analysis. Hierarchical clustering of with all PV patients (PV-A þ PV-R) as well as UMCR (Figure 6). 534 DEGs generated from this comparison separated the 10 samples Functional annotation and pathway analysis of this subset of 155 into their corresponding phenotype (data not shown) with 163 up shared DEGs revealed pathways, processes and networks such as and 371 downregulated transcripts (Supplementary Table 1). The top G-protein signaling-associated RhoA (ARGEF3, EPHA8, GNAQ, 10 DEGs are shown in Figure 4a. See Supplementary Table 4 for the PFAS), RAC1 (ARHGAP12, PFAS, LCP2), and JNK pathways full DEG list with chromosomal locations. (MAP3K5, MAP4K4, MAPK9, MAP3K2), apoptosis (MAP3K5, NSMAF, Functional annotation and pathway analysis along with GNAQ, MAPK9, LEPROT, and SMAD2), cell cycle regulation (CD16), Pubmed literature searches revealed significant enrichment in cytoskeleton remodeling (PFAS, SMAD2), immune response the cell cycle-related pathway, with all eight transcripts down- (PSMA2, ACTN2, ARPC5, MAPK9, LCP2), keratinocyte differentiation regulated in MCR. Pathways such as spliceosomes (eight DEGs), (MAP3K5, GNAQ, SMAD2) cell adhesion (EPHA8, MAP4K4) and JNK (MAPKs 8–10) signaling pathway (five DEGs), CD28 (T-cell- inflammation (ATF1, MAPK9). Based on the discovery of this specific surface glycoprotein CD28) signaling in immune response unique subset of DEGs associated with the PV HLA-related (six DEGs), GnRH (gonadotropin-releasing hormone) signaling in ‘control’ signature that exhibit overlapping, but inverse regulation reproduction (seven DEGs) and muscarinic cholinergic receptor in MCR when compared with either PV patients or UMCR, we (ACM/ACM3)-regulated pathways (six DEGs) were also enriched in propose the existence of a potential ‘protection’ signature in the ‘control’ signature, with the majority of transcripts being individuals who carry PV-associated HLA risk alleles but remain downregulated in HLA-MCR subjects (Figure 4b). disease free. For the full list of 155 DEGs with FCs in MCR vs PV-A and PV-R as well as MCR vs UMCR see Supplementary Table 5. Interestingly, three DEGs (IL13RA1, OGFRL1 and CNPY3) over- The HLA-linked ‘protection’ signature lapped between the ‘protection’ and ‘activity’ signatures and were When analyzing DEGs associated with the ‘disease’ and ‘control’ downregulated in MCR vs all PV patients (PV-A þ PV-R) as well as signatures, we observed an overlap of 158 genes between the two UMCR. Furthermore, these three DEGs were also downregulated in lists (Figure 5a). Remarkably, within the 158 DEGs, a subset of 155 remittent PV patients vs active PV patients.

& 2013 Macmillan Publishers Limited Genes and Immunity (2013) 487 – 499 Genome-wide expression analysis in Pemphigus vulgaris R Dey-Rao et al 492

Figure 4. Functional annotation and pathway analysis of the ‘control’ signature. We compared gene expression proﬁles from four HLA-matched (MCR) vs six HLA-unmatched (UMCR) control subjects generating a trimmed list of 534 DEGs (163 up and 371 downregulated). (a) Top 10 up and downregulated genes in the ‘control’ signature. (b) Functional annotation and pathways analysis reveal signiﬁcant enrichments in pathways and processes related to cell cycle, spliceosomes, JNK, CD28 and GnRH signaling, and muscarinic cholinergic receptors (ACM/ACM3)- related pathways with most transcripts downregulated in MCR. Bars indicate fold up and downregulation of genes. A ‘positive’ FC indicates an upregulation and a ‘negative’ FC indicates a downregulation of genes in HLA-MCR as compared with HLA-UMCR individuals.

Figure 5. Identiﬁcation of a ‘protection’ signature in HLA-matched controls (MCRs). (a) The venn diagram shows an overlap of 158 DEGs between the ‘control’ (534 DEGs) and ‘disease’ signatures (902 DEGs). (b) Principle components analysis (PCA) of a subset of 155 genes out of the 158 overlapping DEGs reveals distinct spatial separation (expression level clustering) in 13 PV-A (blue), 8 PV-R (green) and 6 UMCR (purple), away from 4 MCR (red). The ellipsoids highlight portioning of the different samples. In this three-dimensional plot, the three principle components PC#1, #2 and #3 of the samples and their respective variations are expressed on the x, y and z axis. The total percentage of PCA mapping variability (78%) is shown on top.

A detailed list of ‘disease-,’ ‘activity-,’ ‘control-’ and ‘protection- Conﬁrmation of microarray results ’associated DEGs that have been linked to PV and/or other To conﬁrm the altered expression levels of genes detected by autoimmune diseases is included in Supplementary Table 6. microarray, we performed quantitative real-time PCR of cDNA

Genes and Immunity (2013) 487 – 499 & 2013 Macmillan Publishers Limited Genome-wide expression analysis in Pemphigus vulgaris R Dey-Rao et al 493 derived from the total RNA of selective active and remittent PV The trend of relative FCs in gene expression from quantitative patients and HLA-matched and HLA-unmatched healthy controls reverse transcription PCR was consistent with our microarray (classiﬁed in the exact same manner as the initial set of test samples). ﬁndings (Supplementary Table 7). Although the extent of FCs varied to a degree between the quantitative reverse transcription PCR and microarray results, the directionality of the differential expression patterns was the same in all cases, supporting the reliability of the microarray analysis. A representative example of the results for PTPN22 is shown in Supplementary Figure 1.

PV-associated DEGs overlap with previous microarray studies We next compared DEGs associated with the ‘disease,’ ‘activity,’ ‘control’ and ‘protection’ signatures to previously published PV-related global gene expression signatures in patients and in vitro. The most comparable study was the microarray validation analysis by Sarig et al., who reported 175 DEGs comparing nine newly diagnosed (untreated) PV patients with nine age- and population-MCR individuals. Thirteen DEGs (mostly upregulated) in the ‘disease’ and six DEGs (all upregulated) in the ‘activity’ signatures reported in our study are common to the Sarig et al. study28 (Supplementary Table S8A and B). One DEG, GAS7 (Growth-arrest speciﬁc 7), overlapped between the ‘protection’ signature and the microarray analysis of the Sarig et al. study where it was upregulated in patients vs controls. In our study, GAS7 was also upregulated in PV patients as well as in UMCR when compared with MCR. We also found four dysregulated genes, CD33 (‘activity’ signature), DSC2 (‘activity’ signature), CTSB (‘disease’ signature) and USP34 (‘disease’ signature), which overlapped with three earlier, in vitro microarray studies, which were performed by exposing either human or murine keratinocytes to PV patient serum.29–31

Mapping of DEGs to the genome To better understand the distribution of PV-associated dysregulated genes across the genome, we leveraged our gene expression data to identify transcriptional ‘hot spots’ at which the DEGs map with statistically increased frequency than would be expected by chance using the ‘genome’ tool within the dCHIP software. All significant gene stretches with X4DEGs at p1% false discovery rate (FDR) level were designated as ‘hot spots’. Seven ‘hot spots’ were found within the ‘disease’ signature: 2p16.1-p13.1, 4q13.2- q22.1, 9p11.2-q21.13, 11q14.3-q22, 14q21.3-q23.1, 15q14-q21.1 and 22q11.1-q12.3. Six transcriptional ‘hot spots’ associated with the ‘activity’ signature were found on chromosomes 1q21.1-q23.3, 7q21.1, 11q12.2-q13.5, 19p13.3, 21q22.11-q22.3 and 22q12.2-13.2. Mapping of the 652 DEGs within the ‘control’ signature revealed four significant dysregulated gene stretches on chromosomes 3p14.3, 6p25.2-p21.1, 10q11.1-q11.23 and 16p13.3-p11.2 (Supplementary Figure 2). No ‘hot spots’ were associated at p1% FDR level with the 155 DEGs of the ‘protection’ signature. However, when the criteria of inclusion were changed to p5% FDR, two significant gene stretches with X3DEGs were observed on chromosomes 3p14.3 and 14q21.2-q24.1. Although 60 DEGs across the four PV-associated signatures map to chromosome X

Figure 6. Hierarchical clustering of ‘protection’ signature was processed using the Euclidean distance algorithm in Partek Genomic Suite. In the heat map, red indicates upregulation while blue indicates downregulation and gray indicates no change. The samples cluster into HLA-matched controls (MCR ¼ red) and PV patients and HLA-unmatched controls (PV-A þ PV-R þ UMCR ¼ teal). Two distinct clusters of genes are observed: (1) a group of 132 genes, which are downregulated in MCR when compared with PV- A þ PV-R þ UMCR and (2) a group of 23 genes upregulated in MCR when compared with PV-A þ PV-R þ UMCR. Expression value inten- sities are illustrated by the color of the scale with a range of À 4to þ 4 on a log scale.

& 2013 Macmillan Publishers Limited Genes and Immunity (2013) 487 – 499 Genome-wide expression analysis in Pemphigus vulgaris R Dey-Rao et al 494 Table 2. Chromosomal locations of all 183 dysregulated genes mapping within 19 transcriptional ‘hot spots’ across PV-associated gene expression signatures

Signatures Chromosome #Genes Gene symbol region

‘Disease’ 2p16.1-p13.1 12 ANTXR1, MOBKL1B, MPHOSPH10, PELI1, MXD1, VPS54, PNO1, ELP1P, PPP3R1, USP34, MTIF2, PAPOLG 4q13-q22.1 23 GRSF1, UBA6, YTHDC1, MOBKL1A, MTHFD2L, RUFY3, FGF5, HERC3, SLC4A4, G3BP2, GDEP, MRPL1, NAA11, SCD5, THAP9, COQ2, HELQ, MRPS18C, ADH4, EIF4E, PDLIMS 9p11.2-q21.13 19 LOC100240734, CHMP5, GNE, UBAP2, ELAVL2, PLAA, TOPORS, C9orf72, SMARCA2, KLF9, LOC572558, TJP2, GNAQ, ZCCHC6, C9orf71, FXN, LOC203274, MGC21881, FAM108B1 11q14.3-q22 8 FAT3, CEP57, ENDOD1, FAM76B, FUT4, SLC36A4, KIAA1826, MTMR2 14q21-q23.1 11 MGAT2, PELI2, C14orf106, KLHL28, ATP5S, SDCCAG1, GMFB, MUDENG, MAX, JKAMP, SAV1 15q14-q21.1 10 NANOG, FAM98B, SNAP23, IVD, PDIA3, EIF2AK4, ZFP106, LRRC57, MYO5A, PLDN 22q11.1-q12.3 10 FLJ39632, IL17RA /// LOC100506204, C22orf37,C22orf39, SNAP29, POM121L9P, GGT1, ADRBK2, GAS2L1, LIMK2 ‘Activity’ 1q21.1-q23.3 8 PRKAB2, CTSS, ANKRD20A1 /// ANKRD20A2 /// ANKRD20A3 /// ANKRD20A4 /// ANKRD20B /// LOC375010, ADAM15, SNX27, SEMA4A, CD1C, CD244 7q22.1-7q22.1 4 AP1S1, PILRA, ZNF3, TRIM4 11q12.1-q13.5 11 MS4A6A, CATSPER1, FADS1, ASRGL1, DPP3, ALDH3B1, YIF1A, CLPB, KCNE3, C11orf30, PAK1 19p13.3 4 CFD, C19orf6, POLR2E, MIDN 21q21.1-q22.3 9 C21orf45, OLIG1, KCNJ15, CSTB, NDUFV3, PDXK, SLC19A1, WDR4, C21orf56 22q12.2-13.2 9 OSM, SIRPA, CSF2RB, MPST, NCF4, NPTXR, TST, NFAM1, RRP7A ‘Control’ 3p14.3 5 CCDC66, PXK, APPL1, ARHGEF3, PDHB 6p25.2-p21.1 16 GLP1R, C6orf130, ENPP4, KIAA0240, TIGD1L, TRIM15, RPP21 /// TRIM39 /// TRIM39R, HIST1H2BK, HIST1H2AG, MRS2, KIF13A, EEF1E1, CDYL, LYRM4, WRNIP1, CNPY3 10q11.23-q11 7 CSTF2T, SYT15, FAM21A /// FAM21B /// FAM21C /// FAM21D, NCOA4, LOC220980, ZNF488, LOC100133089 16p13.3-p11.2 15 LYRM1, FLJ21408, ACSM5, C16orf45, C16orf63, MPV17L, SHISA9, CARHSP1, C16orf91, FAHD1, HMOX2, ZNF200, PRSS33, ZNF598, PIGQ ‘Protection’ 3p14.3 3 ARHGEF3, APPL1, PXK 14q21.2-q24.1 7 C14orf106, ATP5S, SAV1, JKAMP, MAX, VTI1B, RDH11 Abbreviation: PV, Pemphigus vulgaris. DEGs in bold overlapped with previously reported PV-related transcripts.

(none mapped to chromosome Y), no transcriptional ‘hot spot’ unmatched). The unanticipated identification of a subset of was found. DEGs that overlaps between the ‘control’ and the ‘disease’ Overall, 183 DEGs were included in the 19 ‘hot spots’ across the signature, but based on opposite directionality of regulation four PV-related signatures (Table 2). Three of these, OSM (‘activity’ separates HLA-MCRs from both HLA-unmatched healthy signature), OLIG1 (‘activity’ signature) and USP34 (‘disease’ individuals as well as active and remittent PV patients–termed signature), overlapped with previously reported PV-related the ‘protection’ signature here–represents an example of ‘class DEGs.28,29 We also uncovered one dysregulated gene, GPD1L discovery’ whereby molecular methods reveal a previously (Chr3p22.3) (‘disease’ signature), upregulated in PV patients vs unknown classification. HLA-MCRs, which did not map to any ‘hot spot’, but coincided Functional annotation and pathway analysis of the DEGs in the with a recently reported potential non-HLA disease susceptibility PV-associated ‘disease’ and ‘activity’ transcriptional signatures gene.28 underscore dysregulated genes and pathways significant to genetic susceptibility, induction, expression and progression of the disease. The G-protein signaling pathway, which includes DISCUSSION RhoA, p38MAPK and JNK enriched in the ‘disease’ signature, is The varied incidence of PV across populations, strong association associated with triggering apoptosis and acantholysis in keratino- with HLA class II subtypes and recent identification of putative cytes.36–40 The kinases in the MAPK pathway, relevant to adaptive disease susceptibility genes provide strong support for a genetic immunity, respond to external stimuli (mitogens, stress and pro- basis for PV.10,32–35 The heterogeneous nature of the disease inflammatory cytokines). Activation of signaling through both makes it necessary to use multivariate techniques in an attempt to protein kinase C isoforms and MAPK has been proposed to initiate make associations between clinical observations and underlying acantholytic changes by affecting changes in desmosomes as well molecular and genetic perturbations. Unbiased analysis of as reorganization in the cytoskeleton.15,29,37,38,41 The large number microarray data from peripheral blood of PV patients and (42 DEGs) of generic/protein kinases and phosphatases found is of control subjects produced unique gene signatures in our study, significance because phosphorylation and dephosphorylation thus revealing a method for molecular classification of patients of key effector molecules and adhesion complexes by them and through ‘class prediction’ (categorizing samples by disease state). phosphatases have been suggested to have a major role in The ‘disease’ signature clearly distinguishes PV patients from acantholysis.42 Alterations in the actin cytoskeleton constitute one genetically related HLA-MCRs, a contrast with the potential to of the major processes proposed to be involved in PV-IgG-induced delineate gene markers relevant to disease induction. The ‘activity’ keratinocyte dissociation. The relevance of these changes in signature separates active from remittent stages of disease, and peripheral blood remains to be interpreted. Overall, the genes within this signature may be important for understanding transcriptional data within the ‘disease’ signature represent a disease progression and prognosis and for identifying biomarkers step forward in delineating diagnostic markers relevant to PV and to monitor treatment success in reverting patients to a lesion- provide insights into mechanisms relevant to disease free state. The PV HLA-related ‘control’ signature distinguishes development. The large number of DEGs between patients and healthy individuals that type as HLA DRB1*0402 and/or controls emphasizes the clinical heterogeneity and multifactorial DQB1*0503 positive (HLA matched) from those that do not (HLA nature of PV.

Genes and Immunity (2013) 487 – 499 & 2013 Macmillan Publishers Limited Genome-wide expression analysis in Pemphigus vulgaris R Dey-Rao et al 495 In the ‘activity’ signature, we observed an overall upregulation of The case for disease relevance of specific genes and pathways transcripts associated with immune mechanisms, inflammatory may be strengthened for genes that are dysregulated across processes, G-protein signaling pathways, oxidation reduction and analyses. There are four DEGs which overlap all four signatures apoptosis that are emphasized in patients with heightened clinical (IL13RA1, KIF13A, OGFRL1 and CNPY3) and are involved in activity as compared with those in a quiescent phase of disease. immune response, B-cell proliferation and IL-13/IL-4 signaling These data establish molecular classification of defined PV clinical pathway in inflammation. Furthermore, genes that are dysregu- states and suggests potential prognostic markers for disease flares. lated across studies may be particularly relevant for disease The observed enrichment of the NOD-like receptor signaling pathogenesis. Thirteen DEGs (ACSL1, PTGS1, CA2, F5, PFKFB3, pathway with four DEGs (PYCARD, NOD2, IL18 and CARD9) reflects FAM198B, GADD45A, GAS7, GPR27, IL1R2, IRAK3, OLIG1 and PDK4) an activated immune system in PV patients with active lesions. The were found common between the ‘disease’ signature reported upregulation of 19 DEGs in the enriched oxidation-reduction here and recent microarray-based transcriptional data by Sarig process may indicate enhanced oxidative stress on-going at the et al.,28 and are involved in enriched pathways and networks systemic level during phases of increased lesional activity. associated with apoptosis, inflammatory and innate immune Interestingly, our group has recently found a statistically significant response, supporting their role in PV pathogenesis.15,17 Six decrease in total antioxidant capacity in serum from PV patients vs common DEGs were found between the ‘activity’ signature control individuals (manuscript in preparation). Finally, the reported here and the Sarig et al. study,28 namely, OSM, CES1, observed upregulation of transcripts in the RAC1-associated CKAP4, DSC2, SLC11A1 and OLIG1. These genes were upregulated G-protein signaling pathway reflects an involvement in cyto- in active patients in both studies and are involved in pathways skeleton remodeling and cell polarity,43–45 which are major and networks associated with immune response, phagocytosis, processes involved in PV-IgG-induced keratinocyte dissociation. cell adhesion and cell junctions. Within the PV HLA ‘control’ signature, we found six dysregu- It has been demonstrated that genes that are highly lated genes in the muscarinic cholinergic receptors, ACM/ACM3- transcriptionally dysregulated are likely to be altered at the DNA related pathways. The cholinergic system has been shown to level.55 It can be expected that in at least some cases modulate intercellular adhesion by targeting cadherins and transcriptional variance observed in disease represent the integrins,46,47 and it has been reported that acantholysis in downstream functional consequences of disease-associated pemphigus can occur because of anti-acetylcholine receptor genetic variations. Chen et al.55 proposed using gene expression antibodies in keratinocyte monolayer.48–50 The significant data to influence the prioritization of target candidate genes for enrichment of the KEGG pathway ‘spliceosomes’ with eight single-nucleotide polymorphism in genome-wide association DEGs was noteworthy due to an autoimmune response against studies. As such, we transposed our transcriptional data set onto essential components within the spliceosomes that is associated the physical genome map and identified 19 transcriptional ‘hot with rheumatoid arthritis and systemic lupus erythematous.51 spots’ representing a total of 183 DEGs associated with the four Most interestingly, of the 158 overlapping DEGs found between PV-related signatures described in this report. Functional the ‘disease’ and ‘control’ signatures, a subset of 155 were annotation and pathway analysis of the 183 ‘hot spots’-related regulated in the opposite direction in MCR when compared with DEGs revealed significant associations with PV-related pathways, either all PV patients or UMCR. Both PCA analysis and hierarchical processes and networks such as apoptosis, cytoskeletal clustering revealed that this set of 155 genes distinguished MCR remodeling, immune and inflammatory response, as well as from all PV patients (PV-A þ PV-R) as well as UMCR. We propose male sex differentiation. Three of these ‘hot spots’-related DEGs that this set of 155 genes may constitute a ‘protection’ signature (OSM, OLIG1 and USP34) were identified in earlier microarray relevant to preventing the expression of PV in MCR individuals analyses in PV.28,29 In addition, GPD1L (glycerol-3-phosphate that carry the highly disease-linked HLA risk alleles DRB1*0402 and dehydrogenase 1-like; FC ¼ 2.1) was the one DEG common DQB1*0503. The dysregulated pathways and processes enriched between our study (‘disease’ signature) and a suspected non- among this subset of 155 DEGs are related to G-protein signaling, major histocompatibility complex susceptibility locus in PV.28 immune response, apoptosis, cell cycle regulation, keratinocyte A defect in GPD1L has been associated with various forms of differentiation, cell adhesion and inflammation, among others inherited arrhythmia.56 None of the other recently reported35 which are central to PV pathogenesis.15,36,45,52–54 putative non-major histocompatibility complex susceptibility loci Within the ‘protection’ signature, several transcripts such as in Israeli Jewish PV patients were found to be transcriptionally ARHGEF3, IL13RA1, SMAD2, PTPN22 and CNPY3 have been shown dysregulated in our study. Transcriptional ‘hot spots’ identified in to have a role in inflammatory and autoimmune diseases. These this study may house as yet unknown PV susceptibility loci. The five molecules are highlighted as examples of genes whose candidates identified from this study may help to inform future upregulation may contribute to operational pathways in PV and research to identify individuals predisposed to PV. For more other autoimmune diseases, and whose downregulation in MCR information on the DEGs with putative roles in either PV and/or whether compared with PV patients or UMCR may indicate a other autoimmune/inflammatory diseases, see Supplementary protective mechanism in genetically susceptible but symptom- Table 6. free individuals. Moreover, IL13RA1, OGFRL1 (opioid growth factor In conclusion, the analyses presented here advance efforts to receptor-like 1) and CNPY3 were found to overlap between the better understand the molecular and genetic basis of autoimmu- ‘protection’ and ‘activity’ signatures and found to be down- nity in the skin. PV is a polygenic and multifactorial disease. The regulated in MCR when compared with either all PV patients or reasons for a breach of immunological tolerance are not known UMCR as well as downregulated in remittent PV patients when but may involve environmental/infectious trigger factors in compared with active PV patients. This may suggest a role of addition to genetic factors and immune dysregulation. Current IL13RA1, OGFRL1 and CNPY3 in both disease protection and in knowledge suggests a working model for PV where self (or cross prevention of lesional activity. Studies using larger cohorts of reaction foreign)-antigen is presented in the context of major patients and healthy controls involving immunohistopathology, histocompatibility complex class II molecules to CD4 þ T cells, analysis of serum protein levels and mechanistic studies which then activate antigen-specific B cells to produce skin- are needed to validate our results and the existence of the specific autoantibodies. Autoantibodies target epidermal antigens ‘protection’ pattern. For DEGs within the ‘disease,’ ‘activity,’ (mainly desmoglein 3, and in some cases desmoglein 1) to cause ‘control’ and ‘protection’ signatures with previously described the classic disease pathology (acantholysis, suprabasal split of the relevance to PV and/or other autoimmune/inflammatory diseases epidermis, blister formation and erosions) by mechanisms not yet see Supplementary Table 6. fully understood. The genes and pathways revealed by genome-

& 2013 Macmillan Publishers Limited Genes and Immunity (2013) 487 – 499 Genome-wide expression analysis in Pemphigus vulgaris R Dey-Rao et al 496

Figure 7. A schematic representation of the disease process in PV supported by some of the differentially expressed genes (DEGs) and pathways revealed by microarray analysis. DEGs from the ‘disease’ signature are listed in black, whereas DEGs from the ‘activity’ signature are listed in red.

wide expression analysis lend support to the steps along this active disease (PV-A) showing the presence of non-transient mucosal or putative disease pathway, revealing dysregulation in innate and cutaneous lesions at the time of blood draw and 8 PV patients in remission adaptive immune processes, responses to viral and bacterial (PV-R) with the absence of non-transient mucosal or cutaneous lesions at 11 molecules, leukocyte and B-cell activation, and cytokine/chemo- the time of draw, 4 healthy control individuals who carry the PV- kine/stress pathway activation, and further illuminate additional associated HLA DRB1*0402 and/or DQB1*0503 risk alleles (‘matched’ controls; MCR) and 6 healthy control individuals not associated with the genes and functional pathways with potential relevance to disease HLA risk alleles (‘unmatched’ controls; UMCR) were used for gene (Figure 7). The role of apoptosis in PV is unresolved to date, but expression analysis. The 21 PV patients included in the analysis were recent data indicate that apoptotic signaling pathways may be further classified as being either off (OFF), on minimal (MIN) or more than 16 activated in the skin after antibody binding. How these minimal (MORE MIN) therapy (Supplementary Table 9) according to phenomena relate to the dysregulation of apoptotic molecules previously published consensus guidelines.11 Minimal therapy (MIN) is in the blood revealed by this study remains to be determined. The defined as less than, or equal to, 10 mg per day of prednisone (or the discovery of specific transcriptional profiles or ‘signatures’ equivalent) and/or minimal adjuvant therapy for at least 2 months. ‘OFF’ assigned to clearly defined patient and control subgroups has therapy is defined as being off all systemic therapy for at least 2 months. clinical relevance to disease classification and provides insights Demographic information, duration of disease and treatment history were obtained from each subject at the time of sampling. The PV patient into disease mechanisms and genetic susceptibility. Extended group consisted of 15 females and 6 males (2.5:1 ratio). The female/male microarray analysis in larger data sets and further defined clinical ratio was maintained in the healthy control group with seven females and subgroups will be needed to validate the expression patterns three males (2.3:1 ratio). The average age of the PV patients was 48.9 years described in this report and identify actionable biomarkers and (±10.3) and that of the controls was 44.9 years (±11.7). There were 25 genetic risk elements with direct utility, perhaps incorporating Caucasians, 1 Asian, 2 African Americans and 3 individuals of Hispanic longitudinal study design. Most significantly, our finding that a set origin included in the study. Full study subject information is provided in of disease-associated genes is downregulated in HLA-matched Supplementary Table 9. individuals as compared with either PV patients or UMCR subjects Ficoll gradients (Amersham Biosciences, Piscataway, NJ, USA) were used suggests the novel and provocative hypothesis that this signature to extract peripheral blood mononuclear cells from collected blood samples. Specimens were immediately frozen at À 80 1C immediately for serves a protective function in genetically susceptible individuals subsequent RNA extraction (see below). and may harbor targets that could be potentially manipulated to thwart, or even reverse, disease. HLA typing HLA allele typing at DRB and DQB loci was completed at the Tissue Typing laboratory of Michigan State University, by PCR amplification using MATERIALS AND METHODS sequence-specific primers as described by Olerup and Zetterquist.57 Patient recruitment and tissue handling Patients and controls were grouped into ‘HLA matched’ (either homo- or The study was approved by the Institutional Review Boards of Weill heterozygous expression of the PV-associated alleles DRB1*0402 and/or Medical College of Cornell University/New York Hospital (IRB 0998-398) DQB1*0503)34 or ‘HLA unmatched’ (expressing any other HLA class II alleles and Michigan State University (IRB 05–1034). Subjects diagnosed with PV than DRB1*0402 and DQB1*0503; see Supplementary Table 9). based on established clinical, histological and immunological criteria were recruited through local patient support group meetings, and the Dermatology outpatient clinics at New York Hospital and Michigan State RNA extraction University. Informed consent was obtained from all study subjects before Procedures for RNA extractions and cDNA synthesis were followed as blood was collected. In total, 31 samples of blood from 13 PV patients with described before.5,58 Briefly, total RNA was isolated from peripheral blood

Genes and Immunity (2013) 487 – 499 & 2013 Macmillan Publishers Limited Genome-wide expression analysis in Pemphigus vulgaris R Dey-Rao et al 497 mononuclear cells using TRIzol reagent (Invitrogen Corporation, Carlsbad, least two to five genes were included. We used a comparative enrichment CA, USA) following the manufacturer’s instructions. Purification of RNA was workflow in MetaCore for the DEG list generated from comparing PV- achieved using an RNeasy Mini kit (Qiagen Inc., Valencia, CA, USA). RNA A þ PV-R vs MCR (‘disease’ signature) with MCR vs UMCR (‘control’ quality was checked by using an RNA Pico Chip with an Agilent 2100 signature) and compared all four signatures with each other as well. Bioanalyzer (Agilent Technologies Inc., Santa Clara, CA, USA) and quantified We analyzed each of DEG lists for chromosomal enrichments by by fiber optic spectrophotometry by Nanodrop ND-1000 (Thermo leveraging our gene expression data to identify regions of the chromo- Scientific, Wilmington, DE, USA) yielding both an A260/A280 absorbance somes with a statistically significant proportion of DEGs, to detect ratio greater than 2.0 and a 28s/18s rRNA ratio equal to or exceeding 1.8. transcriptional ‘hot spots’. The DEGs in each list generated were analyzed Purified RNA was stored at À 80 1C up to 30 days before starting the in the context of all genes on the array for its chromosomal location amplification step. Sixteen micrograms of total RNA from each sample was enrichment using DNA-Chip Analyzer (dCHIP; www.dchip.org). Duplicate used to synthesize the cDNA template. probe sets were masked for gene mapping using the ‘genome’ tool.

Microarray analysis Confirmation of microarray results using quantitative reverse Fragmented biotinylated cDNA were hybridized to Human Genome U133 transcription PCR Plus 2.0 microarray chips (Affymetrix Inc.) for 16 h at 45 1C. The chips were Total RNA from human (patients and controls) blood was made using the then washed, stained and scanned according to manufacturer’s protocol PAXgene Blood RNA Kit (Qiagen Inc.) according to manufacturer’s (Affymetrix Inc.) on the Affymetrix Fluidics Station 750, and scanned by the recommended protocol including DNase treatment. RNA was quantified Affymetrix GeneChip Scanner 3000. The HG-U133 Plus 2.0 gene chip by fiber optic spectrophotometry by Nanodrop ND-1000 (Thermo comprises 1 300 000 unique oligonucleotide features representing more Scientific, Wilmington, DE, USA) and an A /A absorbance ratio X2.0 than 47 000 transcripts representing approximately 39 000 well-character- 260 280 was taken as inclusion criteria. ized human genes We used an entirely new set of PV patients and healthy controls (classified as PV-active, PV-remittent patients, as well as HLA-matched and Microarray data analysis HLA-unmatched healthy controls, in the same manner as in the initial test The Human Genome U133 Plus 2.0 microarray platform was used to set of samples) for this analysis. cDNA was synthesized from 400 ng total generate gene expression profiles from blood samples obtained from a RNA using the Promega Kit (Promega Corp., Madison, WI, USA) and total of 31 patients and controls. Initially, MAS5 statistical algorithm was quantitative real-time PCR was subsequently carried out using the FastStart used for probe set summarization using the Affymetrix Expression Console Universal SYBR Green Master (ROX) from Roche (Roche Diagnostics, software to assess the data quality of all 31 samples through a set of Mannheim, Germany) according to carefully standardized protocols. quality control indicators. No outliers were identified. Gene expression data Intron-spanning primers were designed using Primer3 v4.0 (http:// were then imported to Partek Genomics Suite v6.6 (Partek, St Louis, MO, primer3.wi.mit.edu/) for the following selected DEGs: THAP1, PTPN22 USA) as CEL files using default parameters. Robust multiarray average (‘protection’), TLR4, IL1R2 (‘disease’), PLEKHA1 (‘activity’) and VNN1 (‘control’) based on published human gene sequences in the Ensemble analysis was used to perform raw data preprocessing, which included log2 transformation, quantile normalization, background correction and median Genome Browser (http://useast.ensembl.org/index.html). Amplicons were polish probe set summarization. Average expression levels were distrib- designed to be less than 150 bp in most cases. Reactions were run in uted similarly across all samples. triplicates from PV-A þ PV-R (n ¼ 4), MCR (n ¼ 3) and UMCR (n ¼ 2), and the experiments repeated twice. The resulting reverse transcription-PCR cycle times were normalized against the b-actin (ACTB) housekeeping gene and Unsupervised clustering of variable genes FCs in expression were calculated using the 2 À DDCT method62 relative to Unsupervised hierarchical clustering was performed on the most variable one sample of the control taken as unity. The resulting relative FCs in gene expression were compared with those obtained by microarray analysis. log2 transformed, background corrected and normalized expression values using a flat include cut-off filter of Coefficient of variation (CV)X0.2 across all 31 samples. To study the effect of therapy status on unsupervised clustering of variable genes in the PV-A þ PV-R þ MCR sample group, the hierarchical cluster was overlaid with known therapy status. We used PCA CONFLICT OF INTEREST on the three signatures discovered by unsupervised hierarchical clustering The authors declare no conflict of interest. to check for outliers and to observe specific batch effects based on disease state (PV-active and remittent) and controls (matched and unmatched). Both these unsupervised methods do not consider known sample attributes such as disease activity, state or therapy status while organizing ACKNOWLEDGEMENTS the data. We superimposed sample information using color coding. We are grateful for resources provided by The Research Technology Support Facility and the Genomics Core at Michigan State University. We thank Venice Cercado for technical assistance in extracting RNA and performing the microarray studies. Finally, DEGs and pathway analysis we thank the study participants without whom this study would not have been In the three signatures (‘disease’, ‘activity’ and ‘control’) clearly separated possible. by unsupervised hierarchical clustering, DEGs were detected using one- way analysis of variance model, implemented in Partek Genomics Suite along with Fisher’s Least Significant Difference contrast method used to compare two groups of samples. FC cut-off was 4±2.0 for the ‘disease’ REFERENCES signature, and 4±1.5 for ‘activity’ and ‘control’ signatures. The P-values 1 Gupta VK, Kelbel TE, Nguyen D, Melonakos KC, Murrell DF, Xie Y et al. A globally generated by the comparisons were then corrected using a Benjamini available internet-based patient survey of pemphigus vulgaris: epidemiology and Hochberg59 (step-up) false discovery rate of 1% and 2% for ‘disease’ and disease characteristics. 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