Genome-wide transcriptional profiling of chronic cutaneous lupus erythematosus (CCLE) peripheral blood identifies systemic alterations relevant to the skin manifestation

YGENO-08692; No. of pages: 11; 4C: 2, 4, 5, 6 Genomics xxx (2014) xxx–xxx

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Genome-wide transcriptional profiling of chronic cutaneous lupus erythematosus (CCLE) peripheral blood identifies systemic alterations relevant to the skin manifestation R. Dey-Rao, A.A. Sinha ⁎ Department of Dermatology, University at Buffalo, Buffalo, NY, USA

a r t i c l e

i n f o

Article history: Received 28 July 2014 Accepted 11 November 2014 Available online xxxx Keywords: Microarray Cutaneous lupus Blood Gene-expression Interferon Apoptosis

a b s t r a c t Major gaps remain regarding pathogenetic mechanisms underlying clinical heterogeneity in lupus erythematosus (LE). As systemic changes are likely to underlie skin specific manifestation, we analyzed global gene expression in peripheral blood of a small cohort of chronic cutaneous LE (CCLE) patients and healthy individuals. Unbiased hierarchical clustering distinguished patients from controls revealing a “disease” based signature. Functional annotation of the differentially expressed genes (DEGs) highlight enrichment of interferon related immune response and apoptosis signatures, along with other key pathways. There is a 26% overlap of the blood and lesional skin transcriptional profile from a previous analysis by our group. We identified four transcriptional “hot spots” at chromosomal regions harboring statistically increased numbers of DEGs which offer prioritized potential loci for downstream fine mapping studies in the search for CCLE specific susceptibility loci. Additionally, we uncover evidence to support both shared and distinct mechanisms for cutaneous and systemic manifestations of lupus. © 2014 Elsevier Inc. All rights reserved.

1. Introduction Lupus Erythematosus (LE) is an autoimmune systemic disease with confounding etiology and pathogenesis. It includes a broad spectrum of related disorders, a variety of clinical manifestations with periodic episodes of inflammation and potentially wide-ranging multi-organ damage to the joints, tendons, kidney, lung, heart, blood vessels, central nervous system and skin [1,2]. Due to the variability in clinical presentation of LE there is a concerted effort to evaluate and develop a unified classification for the disease that incorporates recent scientific developments in the field [3]. Ultraviolet light, medications, hormones, stress, viruses, skin trauma and recently tobacco smoking have all been implicated as triggering factors for cutaneous lupus erythematosus (CLE) [4–6]. While there is an agreement that the disease is multifactorial, with a role for genetic, epigenetic, environmental and immunologic factors [7–9], there remains a large gap in knowledge regarding the exact causes, mechanisms and biological interactions that contribute to the dysregulation of normal immune tolerance that ultimately leads to the development of the characteristic autoimmune attack on the skin. Although a large number of genome wide studies have been published in systemic lupus erythematosus (SLE) [10–24], susceptibility loci for CCLE remain unidentified. Moreover, the molecular instigators of ⁎ Corresponding author at: Department of Dermatology, University at Buffalo, 6078 Clinical and Translational Research Center, 875 Ellicott Street, Buffalo, NY 14203, USA. E-mail address: [email protected] (A.A. Sinha).

disease induction and the precise inflammatory pathways leading to local tissue damage remain poorly understood. Systemic changes are likely to underlie skin specific manifestation CCLE, making it important to examine gene expression in the systemic milieu (blood) to identify and characterize putative diagnostic/prognostic markers of the skin specific disease. In spite of the obvious links between the skin and systemic manifestations of LE, skin flares have been observed independent of SLE or individuals may have SLE without any skin disease, suggesting the presence of pathogenetic differences between the two clinical scenarios [4]. Moreover, treatments that are observed to improve skin disease may have minimal or no effect on systemic disease, in both mice and humans [25,26], which makes it imperative to understand specific pathophysiologic pathways involved in both manifestations of LE. Although the extent of CLE (discoid lupus erythematosus, DLE) is not to be associated with the severity of SLE in patients [27], it is known that 80% of LE patients develop cutaneous lesions at some point and close to 20% CLE patients develop SLE, underscoring the likelihood of shared pathways and genetic background as well as distinct disease mechanisms relevant to both diseases [28]. No study has previously used a gene microarray technology to investigate gene regulatory alterations in the peripheral blood of CCLE patients versus healthy controls. We performed a global microarray analyses and have taken a pathway directed approach to define systemic processes relevant for phenotypic distinctions. The aims of the present study were to: 1) establish a molecular classification for CCLE

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Please cite this article as: R. Dey-Rao, A.A. Sinha, Genome-wide transcriptional profiling of chronic cutaneous lupus erythematosus (CCLE) peripheral blood identifies systemic alterations relevant to the skin ..., Genomics (2014), http://dx.doi.org/10.1016/j.ygeno.2014.11.004

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patients guided by unsupervised hierarchical clustering, 2) identify DEGs and analyze enriched dysregulated functional pathways, processes and networks to facilitate a better understanding of the underlying pathomechanisms in CCLE, 3) compare the transcriptional profile data of CCLE blood (case vs. controls) to our previous CCLE skin analysis (lesion vs. nonlesional skin) [29] to distinguish mechanisms at play at the systemic and target tissue level, 4) map the chromosomal locations of disease associated DEGs to help guide future studies aimed at identifying disease susceptibility loci, and 5) compare the CCLE blood transcriptome to previously reported SLE blood associated expression data as well as potential LE risk genes revealed by GWAS studies, aiming to substantiate any overlaps or distinctions between cutaneous and systemic phenotype of the disease. Our strategy facilitates the integration of genetic, biological and clinical data to identify and characterize molecular elements of potential relevance to disease mechanisms in lupus. Systematic gene expression analyses, both at the tissue and the systemic level hold the promise to unravel the final molecular expression of genomic and environmental interaction that constitutes disease initiation and progression in CCLE. Overall, the results of the present study with limited number of samples attempt to extend our earlier finding in CCLE lesional skin analysis of distinct and overlapping genetic susceptibilities to both systemic and cutaneous disease [29] and provide further insights regarding the molecular genetic basis of disease heterogeneity. 2. Results 2.1. Unsupervised hierarchical clustering separates CCLE patients from healthy controls Unsupervised hierarchical clustering separated the samples into two distinct groups: CCLE patients and healthy controls (Fig. 1a), illustrating a specific systemic gene expression signature linked to “disease”. Within the “disease” signature in peripheral blood, we identified a set of 298 non-redundant upregulated genes, (≥1.5 fold change, FC) significantly enriched in Gene Ontology (GO) biological processes (BP) related to response to virus, immune response, negative regulation of nucleic acid

metabolic process, apoptosis, B cell activation, defense response, and inflammation, several of which are central biological processes implicated in lupus (Supplementary Table 1). We next performed principal components analysis (PCA) on Log2 transformed normalized expression values for N12,633 genes to identify outliers and evaluate whether significant batch effects could be observed. CCLE patients clearly separated away from the healthy controls and no batch effects or obvious outliers were observed (Fig. 1b). Taken together, our analyses indicate that unbiased clustering and PCA distinctly separate CCLE patients from the healthy control group and can assign blood transcriptional signatures based on the presence and absence of disease (“disease” signature). 2.2. Differentially expressed genes (DEGs) in blood from CCLE patients versus healthy controls One hundred and thirty-three DEGs distinguish the CCLE patient blood transcriptional profile from healthy controls, (List C generated as described in the Materials and Methods section) and were common to all 5 DEG lists generated using fold change (FC) cut offs between N±1.1 and 1.5 at p-value b 0.05 (Fig. 2a and b). Hierarchical clustering of the trimmed non-redundant 121 DEGs [111 upregulated in patients (UIP) and 10 down-regulated in patients (DIP)] distinctly separated all study samples into their corresponding phenotype (Supplementary Fig. 1; for the full list see Supplementary Table 2) and was used to perform all subsequent analyses. The top up- and down-regulated genes are included in Fig. 2c and d respectively, with interferon related genes, IFI27 (FC = 14.0), IFI44L (FC = 10.6), IFI44 (FC = 7.4), OAS1 (FC = 6.0) showing the highest fold upregulation, and the natural killer (NK) cell associated genes KLRC1///KLRC2///KLRC3 (FC = −2.8) and CLC (FC = −2.8) being the top down-regulated transcripts. KEGG pathway analysis of the CCLE blood DEGs (by DAVID) reflects activated immune-related as well as intracellular protein degradation pathways (including proteasome and lysosome) (Supplementary Table 3a). Biological processes such as cellular defense response, protein ubiquitination, apoptosis, immune response, RNA catabolic process and response to virus among others were ranked high by the odds ratio for

Fig. 1. Unsupervised clustering of CCLE patients and healthy controls. An unsupervised cluster analysis of the 1701 most variably expressed genes across 6 arrays (PBMCs from 3 CCLE patients and 3 age-, sex matched healthy controls) was performed. a) The dendrogram shows that the patients clearly separate from healthy controls b) Principle components analysis (PCA) displays clear spatial separation of variations in expression values in the 2 groups of samples identified by unsupervised hierarchical clustering. In the 3-dimensional plot, the three principle components PC#1, #2 and #3 of all samples with N12,000 probe set IDs and their respective variations are expressed on the x-, y- and z-axes. The total percentage of PCA mapping variability is 81.7%. Each data point represents one sample. The ellipsoids highlight portioning of the different samples. Assignment of samples by color: CCLE (orange) and CONTROL (blue) Abbreviation: CCLE, Chronic cutaneous lupus erythematosus patient sample; B, Blood, NL, Normal control sample (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

Please cite this article as: R. Dey-Rao, A.A. Sinha, Genome-wide transcriptional profiling of chronic cutaneous lupus erythematosus (CCLE) peripheral blood identifies systemic alterations relevant to the skin ..., Genomics (2014), http://dx.doi.org/10.1016/j.ygeno.2014.11.004

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Fig. 2. Differentially expressed genes in CCLE patients. Differentially expressed genes (DEGs) were detected using 1-way ANOVA along with Fisher's Least Significant Difference (LSD) contrast method to compare peripheral blood gene expression profiles of 3 CCLE patients vs. 3 healthy controls. Controlling p-value at b0.05 were first generated a) 5 lists with fold change (FC) cut offs from N±1.1–1.5. Trimmed lists were then generated by removing “redundant” genes i.e. the same genes identified by different probes. Up = the total # of up-regulated genes, down = the total # of down-regulated genes. b) Venn diagram of the 5 DEG lists demonstrate that List C (FC N ±1.5; p-value b0.05 with 133 DEGs) is at the intersection of all the other lists. All downstream analyses are performed on this list. c) Top 10 up-regulated genes. d) Top 10 down-regulated genes. Abbreviations: FC, Fold change CCLE patients vs. healthy controls.

enrichment (Supplementary Table 3b). MetaCore analysis also revealed several upregulated genes included in the enriched processes reflecting activated antigen presentation in immune response, cellular macromolecule catabolic process, proteolysis (cell cycle and apoptosis as well as ubiquitin–proteasomal proteolysis), leucocyte chemotaxis and defense response in patients (Supplementary Fig. 2). Apoptosis was one of the top 10 enriched dysregulated processes (similar to our previous CCLE skin transcriptional analysis) with 44 DEGs (40 UIP and 4 DIP) (Fig. 3a). Twenty-three dysregulated genes (20 UIP and 3 DIP) were related to the type I interferon (IFN) pathway (Fig. 3b), among the 46 DEGs (40 UIP and 6 DIP) involved with immune system response (Fig. 3c). There were 59 DEGs (54 UIP and 5 DIP) related to stress response (Fig. 3d). 2.3. Comparison of CCLE blood and skin transcriptional profiles Upon comparing the present analyses (CCLE-blood) to our previous CCLE-lesional skin study [29], we found 31 DEGs common between them. (Supplementary Table 4) Enrichment analysis of these shared genes revealed KEGG pathways such as antigen processing and presentation and natural killer cell mediated cytotoxity, GO biological processes such as defense, innate and adaptive immune response, response to virus and apoptosis (Supplementary Table 5) as well as GO molecular functions such as carbohydrate-, peptide- binding and scavenger receptor activity, (not shown) among others. Seven DEGs (CASP10, CEBPG, CTSL, NCOR2, OAS1, STAT1 and PRDX1) are related to the biological process “apoptosis”, and 11 DEGs (IFI30, ITGB2, KLRC1///KLRC2, LILRB4, OAS1, OAS2, STAT1, NCOR2, CEBPA, CEBPG and CCR2) related to type I IFN. The majority of the 31 shared genes demonstrate a common

directionality in up- or down- regulation in both blood and skin. However, nine transcripts (PLIN2, PRDX1, GLB1, CD1D; CD1A, AHCYL1, CEBPG; CEBPA, GNLY, KLRC1///KLRC2, and KLRC1///KLRC2///KLRC3) are dysregulated in opposite directions. Of note, KLRC1///KLRC2, KLRC1///KLRC2///KLRC3 and GNLY, which are part of the enriched “defense response”, were downregulated in blood while being upregulated in the lesional skin of CCLE patients. Comparative analysis of the two transcriptional profiles (CCLE blood and skin) by MetaCore revealed aberrations related to an activated immune response including antiviral actions of interferon, G-CSF-induced myeloid differentiation, IL-5 signaling and antigen presentation by MHC class II among the top 10 enriched canonical pathways. GO processes related to defense-, immune-, and viral- responses were enriched as well as process networks such as chemotaxis, cell adhesion and inflammation, among others. (Supplementary Fig. 3) We further used the “analyze network” algorithm to create enriched, interactive networks that were common as well as unique to the blood (121 DEGs) and lesional skin (776 DEGs) gene expression profiles [29]. (Supplementary Material and Methods online, Section 4.5) The top interactive networks common to both blood and skin DEGs were related to processes such as interferon-gamma (IFN-γ) mediated pathway (STAT1, IFI30, OAS2, PLSCR1, OAS1) (Fig. 4a, and Supplementary Table 6-A1), viral infection (ANP32E, GARS, LILRB4, ARL4C, NDC80), activated T cell proliferation (LGALS3, LGALS3BP, THY1, CASP10, GART), as well as response to lipopolysaccharides, all linked to LE pathogenesis [30–33]. We also discovered networks associated with DEGs unique to the CCLE blood profile (not found in skin) corresponding to processes such as cell surface receptor signaling pathways; response to oxygen containing compound (PGF, ACOT13, KDR, JUN, FLT4) (Fig. 4b and Supplementary Table 6-B1),

Please cite this article as: R. Dey-Rao, A.A. Sinha, Genome-wide transcriptional profiling of chronic cutaneous lupus erythematosus (CCLE) peripheral blood identifies systemic alterations relevant to the skin ..., Genomics (2014), http://dx.doi.org/10.1016/j.ygeno.2014.11.004

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Fig. 3. Apoptosis, type I interferon, stress and immune system related CCLE blood DEGS. A robust signal (a) 44 DEGs associated with apoptotic pathways (b) 23 DEGs associated with the type I interferon mediated pathways c) 46 DEGs with immune system response d) 59 DEGs associated with stress response is found among the CCLE blood DEGs. In the heat map, red indicates upregulation while blue indicates down-regulation and gray indicates unchanged expression. The samples cluster into CCLE patients (orange) and healthy controls (blue). Expression value intensities are illustrated by the color of the scale with a range of −2.0 to +2.0 on a log scale. Abbreviations: CCLE, Chronic Cutaneous Lupus Erythematosus; B, Blood; NL, normal control (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

as well as nucleosome assembly and mitotic DNA integrity checkpoint. Networks associated with genes unique to the CCLE lesional skin profile were linked to processes such as enzyme linked receptor protein signaling pathway (GRB2, FYN, EFNB3, EGFR, RPS6KA1) (Fig. 4c and Supplementary Table 6-C1), apoptosis (CASP8, BAX, BIRC2, GZMB, FAS (CD95) (Fig. 4d and Supplementary Table 6-C2) as well as antigen processing and presentation of endogenous peptide antigen via MHC class I via ER pathway. For the full list of interactive networks generated through “analyze networks” see Supplementary Table 6A–C.

2.4. Mapping of CCLE blood DEGs to the genome We used the “genome” tool in dCHIP to pinpoint the chromosomal regions where DEGs cluster more frequently than would be expected by chance. Examination of the distribution of the 121 CCLE DEGs identifies four transcriptionally active chromosomal regions (“hot spots”): 3p21– p21.3 (GLB1, CX3CR1, CCR2 and RHOA), 5q31–q32 (H2AFY, CTNNA1 and CD14); 15q14–q21.1 (TMEM87A, EIF3J and SPG11) and 22q13.1–q13.2 (HMOX1, LGALS2, LGALS1, ATF4 and APOBEC3G. (Table 1) The fifteen DEGs mapping within the “hot spots” are significantly related to defense response, positive regulation of NF-kappaB cascade, cell death, carbohydrate binding, and chemokine signaling pathway. Two of the 4 blood “hot spot” regions overlap with the 13 lesional skin-associated “hot spot” regions identified in our previous work [29], (Fig. 5) corresponding

to 3 common DEGs (CCR2 (chr3p21.31), LGALS2 (22q13.1), and APOBEC3G (chr22q13.1–q13.2). 2.5. Comparison of CCLE blood DEGs to SLE To gain insights regarding the relationships of CCLE to systemic disease we compared the blood DEGs identified in our present analysis with a comprehensive set of SLE blood DEGs compiled from previous microarray studies and meta-analyses [34–45]. Nine (CCR2, CD14, CTNNA1, EIF3J, H2AFY, HMOX1, LGALS2, RHOA, TMEM87A) of the 15 DEGs located within CCLE transcriptional “hot spots” on chromosomes 3, 5, 15 and 22 overlap with previously published SLE-associated dysregulated genes or polymorphisms. [37,39,42,43,45–47] (Table 1) Furthermore, fifteen CCLE blood DEGs which do not map within chromosomal “hot spots” (IFI44, IFI44L, PLSCR1, EIF2AK2, STAT1, OAS1, OAS2, RNASE2, LGALS3BP, CD163, TIMP1, PIGF, ADA, KLRC1///KLRC2, and TNFSF13) were also found to be differentially expressed in at least one previous SLE gene expression study [37,42,43,45,47–50]. Among the 24 SLE-CCLE overlapping blood DEGs, several were found to be dysregulated in the enriched pathway related to type I interferon related immune response (CCR2, IFI44L, OAS1, OAS2, CD14, IFI44, IFI44L, EIF2AK2, KLRC1///KLRC2, STAT1 and TIMP1) as well as other immune, inflammation, cell adhesion and leukocyte chemotaxis related pathways and processes. As expected, the highest enrichment in diseases by odds ratio in this set was SLE (Fig. 6). CD14, a surface antigen expressed on monocytes and macrophages and a

Please cite this article as: R. Dey-Rao, A.A. Sinha, Genome-wide transcriptional profiling of chronic cutaneous lupus erythematosus (CCLE) peripheral blood identifies systemic alterations relevant to the skin ..., Genomics (2014), http://dx.doi.org/10.1016/j.ygeno.2014.11.004

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Please cite this article as: R. Dey-Rao, A.A. Sinha, Genome-wide transcriptional profiling of chronic cutaneous lupus erythematosus (CCLE) peripheral blood identifies systemic alterations relevant to the skin ..., Genomics (2014), http://dx.doi.org/10.1016/j.ygeno.2014.11.004

Fig. 4. Networks common and unique to CCLE blood- and lesional skin DEGs. We used a comparative analysis of the blood (121 DEGs) and lesional skin (776 DEGs) gene expression profiles. The “analyze network” algorithm in MetaCore creates enriched, interactive networks that are common and unique to the two lists. This algorithm in MetaCore uses all network objects found for both compared lists as the input, along with enrichment and relative saturation to generate a) The top network from common DEGs in both lists (network name; STAT1, IP-30 (IFI30), OAS2, PLSCR1, OAS1; see also Supplementary Table 6, A1) b) Top network unique to the 121 CCLE blood profile (network name; PLGF (PGF), THEM2 (ACOT13), VEGFR-2 (KDR), cJun (JUN), VEGFR-3 (FLT4); see also Supplementary Table 6, B1) c) and d) Top 2 networks unique to the 776 CCLE lesional skin profile c) (network name; GRB2, Fyn (FYN), Ephrin-B (EFNB3), EGFR, p90Rsk (RPS6KA1); see also Supplementary Table 6, C1) and d) (network name CASP8, Bax (BAX), c-IAP1 (BIRC2), GZMB and FasR (FAS); see also Supplementary Table 6, C2). Networks are generated with 50 network objects each, that are prioritized by the number of fragments of canonical pathways in them. Thick cyan lines indicate canonical pathway fragments. Upregulated genes are marked with red circles and down-regulated genes with blue circles. Note: Network name is a partial listing of the network objects included in each network. Types of objects in the network and their expression levels and interactions are clarified in the legend (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.) 5

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Table 1 Chromosomal locations of the 4 transcriptional “hot spots” found across the CCLE “disease” related transcriptional signature. CLE-BLOOD DEGs Chromosome region

# Genes

Gene symbol

3p21–p21.3 5q31–q32 15q14–q21.1 22q13.1–q13.2

4 3 3 5

GLB1, CX3CR1, CCR2, RHOA H2AFY, CTNNA1, CD14 TMEM87A, EIF3J, SPG11 HMOX1, LGALS2, LGALS1, ATF4, APOBEC3G

CCLE blood DEGs corresponding to each “hot spot” are noted. Bold: Nine CCLE DEGs also reported as SLE DEGs. Polymorphism in the promoter region of HMOX1 (underlined) has been previously demonstrated to be associated with childhood-onset SLE.

mediator of innate immune response was 1.9 fold upregulated. Increased levels of the soluble form of CD14 have been observed in SLE patients [51] which closely parallel the clinical course [52]. Additional enriched GO biological processes relate to apoptosis (including HMOX1, TNFSF13, EIF2AK2, STAT1, RHOA, ADA and CD14), positive regulation of B cell activation (with TNFSF13, ADA) and reactive oxygen species (with HMOX1, STAT1 and ADA) among others (Supplementary Table 7). Increased levels of oxidative and nitrosative stress markers have also been shown to be expressed in serum of SLE patients as well as CLE skin lesions [53,54] and is becoming an area of active research. There is a high likelihood that the increased oxidative and nitrosative stress leads to production of increased amounts of reactive oxygen and nitrogen species which then go on to posttranslationally modify host proteins, thus potentially provoking an autoimmune reaction against modified self-proteins [55,56]. For the full list of the 24 DEGs that overlaps with the CCLE blood DEGs and previously published SLE associated gene expression data, see (Supplementary Table 8).

Fig. 5. Genome-wide chromosomal distribution of CCLE blood DEGs.) Gene expression data is leveraged to locate transcriptional “hot spots” in chromosomes. Chromosomal locations of the 121 differentially expressed genes in the CCLE-blood related transcriptional profile are colored in bold black and blue vertical bars versus the other genes which are gray. The vertical bars above and below the horizontal lines represent genes either on the forward or reverse strand. Each horizontal line corresponds to one chromosome. Significant stretches at p-value 0.01, considered transcriptional “hot spots” in the CCLE blood “disease” signature are marked by 4 red boxes. Two of these CCLE blood “hot spot” regions on chromosomes 3 and 22, are shared with our previously reported 13 transcriptional “hot spots” from the CCLE lesional skin transcriptional profile, which have been overlaid on the blood chromosomal map (brown boxes) (For interpretation of the references to color in this figure legend, the reader is referred to the web version of this article.)

To extend our genetic analysis, we delineated 110 potential SLE susceptibility loci by examining SNPs which reached genome wide significance from GWAS studies [10–22] recorded in the National Human Genome Research Institute (NHGRI) catalog (http://www.genome.gov) and the SLEGEN study, including susceptibility loci linkage to SLE [46,57, 58]. We found a single dysregulated gene (HMOX1; chr22q12|22q13.1) mapping within a CCLE blood “hot spot” that was also previously reported as a genetic risk element in childhood-onset SLE in a Mexican population (Table 1) [46]. 3. Discussion LE is a protean disease where systemic changes are likely to direct/ underlie organ specific manifestations. Clinical expression of skin involvement in LE is extremely heterogeneous and the molecular basis for phenotypic classification, disease initiation and progression, or response to treatment is poorly understood. Given the fact that nearly half of the CLE patients experience some form of vocational handicap, improved tools for early diagnosis, treatment, and prognosis are imperative for raising standards of living and socioeconomic outcome for patients. To this end, genome-wide microarray analysis has been utilized in this study along with a pathway-based approach to correlate clinical/genetic attributes with differential gene expression. To better understand the systemic milieu responsible for cutaneous disease development and the molecular basis of phenotypic heterogeneity, we examined the transcriptional profile from peripheral blood of 3 CCLE cases and 3 healthy controls. We demonstrate by unbiased hierarchical clustering that CCLE patient blood can be distinctly separated from healthy control blood, representing an example of “class prediction”. Potentially, such a transcriptional profile could serve a useful tool for improved and minimally invasive diagnostics. DEGs were established from a comparison of the two sample groups and a “disease” based signature was able to distinctly separate all study samples into their corresponding phenotype. Although there is an equal distribution of African-American ethnicity within our patient and control group (n = 6), we cannot discount the effect of ethnicity on gene expression profile and autoantibody formation as has been demonstrated earlier in SLE [59,60]. Future experiments with larger sample size would allow us to investigate a similar and intriguing effect of genetic ancestry on CCLE. Functional annotation and pathway analysis of the CCLE blood DEGs was performed to provide deeper insights regarding disease mechanisms. Our data illuminate processes linked to immune response, defense response, response to virus, cell death, proteasomal proteolysis and cellular macromolecule catabolic processes among others. Many of these correspond to processes associated with the set of upregulated genes in the “disease” related signature generated by unsupervised cluster analysis, (Supplementary Tables 1 and 3) as well as some of the pathways and processes revealed by our previous transcriptional analysis of CCLE lesional skin [29]. Notably, out of the 121 DEGs identified in CCLE blood, we observed significant enrichments associated with apoptosis, type I IFN signaling as well as a generally activated immune response. These themes were also highlighted in our previous lesional skin analysis and are considered central to lupus pathogenesis [8,29, 38,61–65]. Although the number of samples included in the analysis is limited, finding common pathways and processes linked to lupus serves to substantiate the disease relevance of the suggested mechanisms revealed in our CCLE blood analysis. Apoptosis is required for disposal of the enormous burden of selfantigens during normal cell turnover. Thus, defects in apoptotic pathways may be relevant for initiation of autoimmunity [62,66–68]. Cellular debris accumulates either due to increased generation and/or defective clearance of apoptotic debris [69], and may thus facilitate autoantigen presentation that promotes a breach of self-tolerance induction and maintenance. Of interest, both anti-apoptotic (e.g.; ADA, ANXA5, CTSB, HMOX1, PSMA4, PSMA5, and TIMP1) and pro-apoptotic (e.g.; ATF4, CASP10, CCR2, CD14, CEBPG, OAS1, PRDX and STAT1) genes

Please cite this article as: R. Dey-Rao, A.A. Sinha, Genome-wide transcriptional profiling of chronic cutaneous lupus erythematosus (CCLE) peripheral blood identifies systemic alterations relevant to the skin ..., Genomics (2014), http://dx.doi.org/10.1016/j.ygeno.2014.11.004

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Fig. 6. Functional annotation of CCLE blood DEGs that overlap with SLE. Twenty four CCLE blood DEGs were found to overlap with previously reported SLE-associated genes. Pathway analysis and functional annotation of these 24 DEGs demonstrate an enrichment of the following MetaCore categories: a) Canonical Pathway Maps, b) Process Networks and c) Diseases (by Biomarkers). Top ten enrichments are sorted and ranked by p-value shown on a log scale. A lower p-value indicates higher statistical relevance.

are dysregulated in CCLE blood, reflecting either redundancy of participants, participation of several apoptotic pathways concurrently, and/or the contribution of different blood cell types as players in disease. The dysregulated genes also demonstrate an upregulation in catabolic processes and proteosomal proteolysis which highlights a breakdown of macromolecules (DEGs including: LDLR, UBE3A, MAGOH, PPP2R5C, RNASE6, RNASE2, PPT1, OAS2, ZFP36L2, PSMB7, PSMA6, PSMA5, PSMA4, DDB2, PSMD8, CUL1 and BARD1), as well as in DNA damage checkpoint (DEGs including: CUL1, APOBEC3G, PSMB7, RIN2 and TRIM22) which may all relate to the inflammatory response and tissue injury caused by neutrophils and macrophages during disease development and may be actively involved in the formation of the recently reported proinflammatory microvesicles (of neutrophil origin) associated with apoptotic blebs in patients with CLE [6,70]. Interferons modulate antiviral, antiproliferative and immunomodulatory functions of cells. The pathological consequences of increased production of interferon α and its pivotal role during development of SLE are well documented [9,42,44,71]. The role of IFN in the pathogenesis of SLE has also been suggested by data from GWAS studies which have identified lupus-associated genetic variants in components of the innate immune response signaling pathways [72,73]. The pathogenesis of cutaneous lupus is also linked to type I IFN [8,29,74–76]. Apart from their well-known role in innate immunity against viruses, interferon related processes are also involved in adaptive immunity directly affecting T- and B- cell activation and can stimulate the Th1 pathway [67]. We discovered 19% of the “disease” related transcriptional profile in CCLE blood to be associated with type I interferon regulated processes and pathways. Although we did not observe dysregulation of IFN-α itself in the present study, 5 DEGs (STAT1 (FC = 2.1), EIF2AK2 (FC = 2.7), OAS1 (FC = 6.0), OAS2 (FC = 1.9) and PLSRC1 (FC = 2.6)) were included in the top enriched canonical pathway map related to antiviral actions of type I interferon. Upon comparison of the CCLE blood gene expression profile and our previous lesional skin analysis, 31 DEGs are shared. The common DEGs set is significantly associated with perturbations in apoptosis (including both pro- and anti- apoptotic DEGs), as well as immune, defense, inflammatory, T-cell activation, and Type I IFN associated immune-related pathways, among others. The most significantly enriched dysregulated canonical pathway found in this subset of DEGs involves G-CSF induced myeloid differentiation into mature neutrophils. The common dysregulated pathways and processes in the present blood and previous lesional skin analyses clearly indicate that some similar disease-related mechanisms are at play both within the target tissue and systemic milieu in CCLE. While most of the DEGs shared between CCLE blood and lesional skin demonstrate a common directionality in regulation, a subset of 9

genes was regulated in the opposite direction across tissue type. These genes were enriched in pathways and processes such as defense and immune response, which are recurrent themes in both analyses. A down-regulation of KLRC1///KLRC2, KLRC1///KLRC2///KLRC3 (FC = −2.3 and −2.8, respectively) and GNLY (FC = −2.7) is observed in blood, opposed to an upregulation observed previously in the lesional skin (FC = 1.9, 2.6 and 2.5, respectively). GNLY was also found to be upregulated in the DLE skin by Jabbari et al. (DLE vs. psoriasis) [75]. The downregulation of KLRC1///KLRC2, KLRC1///KLRC2///KLRC3 and GNLY (located in the cytotoxic granules of T cells) in blood might reflect the recruitment of NK cells as cytotoxic T lymphocytes into the skin (target tissue). These data highlight KLRC1///KLRC2, KLRC1///KLRC2///KLRC3 and GNLY as examples of mediators that could have tissue-specific local effects (in skin) which differ from systemic effects (in blood). We also observed fewer DC and NK cell associations in the CCLE blood profile as opposed to lesional skin, which again might reflect the migration of these cells into inflamed diseased skin lesions. Additionally, far fewer components of the complement cascade were found dysregulated in blood than our previous lesional skin analysis. The discrepancies in dysregulated processes and pathways between the local and systemic environment underscore underlying differences in pathomechanisms of the disease in the two physiological milieus, which clearly requires further scrutiny. The evaluation of genetic expression datasets can be leveraged to provide insights regarding genetic predisposition to disease. In particular, DEGs may constitute an enriched pool of putative disease susceptibility loci [77–79]. It is not yet known whether genetic susceptibility to SLE overlaps, or is distinct from cutaneous disease. Our recent transcriptome analysis of CCLE lesional skin suggested that it may be both. In support of this, we identified HMOX1, (within the “hot spot” region in chromosome 22) polymorphisms in which, have been found to be statistically significantly related to childhood-onset SLE [46]. Furthermore, in the present study we identified 24 CCLE blood DEGs (within and outside of “hot spots”) which overlap with previously published SLE blood associated DEGs. These 24 overlapping genes reveal enriched functional annotations shared between the two disease manifestations such as activated apoptosis, immune/inflammatory response, type I interferon mediated pathways, and response to oxidative stress. The finding of apoptosis and type 1 interferon response in both systemic and cutaneous lupus highlights similar disease mechanisms at play in the pathogenesis of both cases, supporting previous literature as well as our own study in CCLE lesional skin [4,29,34,42,58,64,65,67,71,75,76, 80–85] skin The remaining 80% CCLE blood “disease” profile (97 DEGs out of 121) which do not overlap with SLE may represent a gene set particularly relevant to skin specific disease processes. Associated functional pathways include antigen processing and presentation, ubiquitin–protein

Please cite this article as: R. Dey-Rao, A.A. Sinha, Genome-wide transcriptional profiling of chronic cutaneous lupus erythematosus (CCLE) peripheral blood identifies systemic alterations relevant to the skin ..., Genomics (2014), http://dx.doi.org/10.1016/j.ygeno.2014.11.004

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ligase activity during mitosis, proteasomal proteolysis (important in cell cycle, tissue injury and apoptosis), cellular macromolecule catabolic processes, and response to stress. We note that stress related molecules (HERPUD1, ATF4, SHFM1, DDB2, VCAN, MYOF, PRDX1, SETX and BARD1) were all upregulated in CCLE blood, perhaps characteristic of the hypermetabolic processes involved in this inflammatory autoimmune disease. These markers could potentially be useful for early detection, diagnosis and prognosis of the disease to guide early therapeutic decision making in patients with cutaneous disease. In conclusion, this study establishes a molecular classification for defined disease pathology in CCLE patients and helps in identifying potential diagnostic and prognostic markers from peripheral blood. Using a pathway-based approach it is possible to correlate clinical/genetic attributes with differential gene expression and derive insight into a broad range of biological processes and pathways underlying disease mechanism. This facilitates progression beyond the identification of disease associated genetic elements relevant to disease and towards functional significance. While unbiased hierarchical clustering analyses could highlight the use of microarray analysis for disease classification, the large number of DEGs between patients and controls underscores the clinical heterogeneity and multifactorial nature of the disease. We found prominent signatures of interferon, apoptosis, proteolysis and stress related pathways in the blood, in corroboration with previous literature (in both SLE and CLE). The analysis of unfractionated PBMCs offers a global, comprehensive viewpoint of disease associated transcriptional changes that may be particularly relevant for disease mechanism and identification of biomarkers. Although, a large number of studies, including ours, have used mixed cell populations (found in PBMCs) to detect differential cell expression, several limitations are associated with using unfractionated blood cells for transcriptional profiling [44,77,86–88]. Future studies, using cell sorting in combination with expression profiling will be necessary to assign putative disease associated genes to specific cell types. Ultimately, clinical and pathogenetic relevance of disease associated gene markers will require validation studies extended to larger cohorts across varied clinical subtypes. Further refinement of disease linked “signatures” can be expected to facilitate the development of advanced tools with clinical utility for determining disease classification, diagnosis, prognosis and response to therapy. Finally, through the merging of global gene expression with genetic datasets, our study provides new insights regarding the genetic basis of clinical heterogeneity in lupus, supporting both shared and unique susceptibility to systemic and cutaneous disease.

from the collected blood samples. We froze the PBMCs immediately at −80 °C for subsequent RNA extraction (see below). 4.2. Total RNA extraction and biotinylated cDNA preparation Similar procedures for RNA extractions and cDNA synthesis were followed as described before [78]. For additional details see Supplementary Material and Methods online. 4.3. Microarray analysis Microarray assays were performed according to the Affymetrix GeneChip Expression Analysis Technical Manual. DNA was purified and concentrated by ethanol precipitation. In vitro transcription assays were performed to produce Biotinylated cRNA which was then fragmented to 50–200 nucleotides. Hybridization with labeled cRNA was performed for 16 hours at 45 °C to microarrays (HG-U95Av2). The chips were subsequently washed, stained and scanned according to manufacturer's protocol on the Fluidics Station 750, and scanned by the GeneChip Scanner 3000 (Affymetrix Inc., Santa Clara, CA, http://www.affymetrix.com). There are upwards of 12,600 probe sets representing sequences previously characterized in terms of function or disease association. For additional details see Supplementary Material and Methods online. 4.4. Unsupervised clustering of variable genes Unbiased analyses were conducted similar to the procedure detailed in our previous report. [29] Briefly, unbiased hierarchical cluster analysis was performed with the most informative probe sets. A probe set was included in the clustering if the coefficient of variation was greater than 0.12 across all skin arrays. 1943 probesets representing 1701 unique genes ‘passed’ the filter criteria. An unsupervised two way cluster analysis was performed on the probe sets that passed the filter in Partek Genomic Suite v6.6 using centroid linkage for samples and average linkage for probesets. A one-way analysis of variance (1-way ANOVA model) was conducted on the entire 12, 625 probeset IDs comparing CCLE patients with healthy controls. From the 1701 probesets that had passed the CV filter in the unsupervised cluster, we found a set of 298 unique genes that were upregulated (N ± 1.5 fold) in CCLE patients. We next used principal components analysis (PCA) on the expression values of all 12, 625 probeset IDs in the samples to check for outliers and to observe batch effects. The unsupervised clustering analyses (including hierarchical clustering and PCA analysis) do not consider known sample attributes while organizing the data. We color coded the superimposed sample information.

4. Materials and methods 4.5. DEGs and pathway analysis 4.1. Patient recruitment, tissue procurement and handling The study was approved by the Institutional Review Boards of Weill Medical College of Cornell University/New York Hospital (IRB 0998–398). Patients diagnosed with CCLE and more specifically, the most common subtype, DLE based on established clinical criteria (ACR, Gilliam classification) were recruited into the study from the Dermatology Outpatient Clinic of New York Presbyterian Hospital, Cornell University. We obtained an informed consent from each subject before the blood was collected. In total, 6 blood samples were used in this analysis, from 3 CCLE (DLE) patients with cutaneous lesions and 3 age and sex matched healthy controls. None of the 3 patients were positive for ANA or met any criteria for SLE. No systemic or topical medications had been used by any of the patients for 2 months prior to sampling. Duration of disease at the time of blood draw, ethnicity, and other demographic data can be found in Table 2. Two patients (LE 1008B and LE1009B) had also provided biopsies and were included in our CLElesional skin study. Ficoll gradients (Amersham Biosciences, Piscataway, NJ) were used to extract peripheral blood mononuclear cells (PBMCs)

Differentially expressed genes (DEGs) between CCLE patient and healthy control arrays were defined as described in our previous report on CCLE lesional skin analysis [29]. From the 1-way ANOVA model on all background subtracted, normalized, log2-transformed expression indices (12, 625 probesets), implemented in Partek Genomics Suite we combined the Fisher's Least Significant Difference (LSD) contrast method that is used to compare 2 groups of samples. We then controlled the p-value at b 0.05, and used a fold change (FC) cut off from ≥± 1.1 to ≥±1.5 to generate 5 DEG lists (Fig. 2a). After removing redundancies from the List C with 133 DEGs (generated by imposing a N ± 1.5 fold change cut off, with an associated p-value b 0.05), a trimmed list of 121 DEGs was used as the “short” list for downstream analyses. This was the “disease” related signature. For additional details see Supplementary Material and Methods online. The DEGs generated were analyzed for their chromosomal location enrichment using DNA–Chip Analyzer (dCHIP) (www.dchip.org) as has been done in previous work from our laboratory [29,77–79,86,89]. To generate “hot spot” regions of enhanced transcriptional activity

Please cite this article as: R. Dey-Rao, A.A. Sinha, Genome-wide transcriptional profiling of chronic cutaneous lupus erythematosus (CCLE) peripheral blood identifies systemic alterations relevant to the skin ..., Genomics (2014), http://dx.doi.org/10.1016/j.ygeno.2014.11.004

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Table 2 Demographic data for study subjects. Patient ID#

Disease status

Gender

Ethnicity

Sample

Disease duration (yr.)

Age (yr.)

Past medications

LE1008B LE1009B LE1011B NL1004B NL1013B NL1014B

Diseased-CCLE Diseased-CCLE Diseased-CCLE Control Control Control

F F F F F F

AA HI AA AA AA C

Blood (PBMC) Blood (PBMC) Blood (PBMC) Blood (PBMC) Blood (PBMC) Blood (PBMC)

11 38 3 N/A N/A N/A

25 50 30 35 37 43

Clobetasol propionate gel, foam None Steroid ointment, flucinolone None None None

Abbreviations: CCLE, Chronic cutaneous lupus erythematosus; NL, normal control; B, blood; F, female; AA, African American; HI, Hispanic, C, Caucasian; Yr., Years; ID#, Identity Number. No patients met criteria for SLE. Patients were under no systemic or topical medications for two months prior to sampling.

using the probeset IDs from the 121 DEGs list from CCLE blood, we set the p-value cut off to b0.01. We masked duplicate probe sets for gene mapping using the “genome” tool [90]. Supplementary data to this article can be found online at http://dx. doi.org/10.1016/j.ygeno.2014.11.004. Conflict of interest

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The authors declare no conflict of interest. Acknowledgments This work was supported in part by grants from the Mary Kirkland Center for Lupus Research; Colleck Research Fund, Weill-Cornell Medical College; and the Colgate-Palmolive, Co. to AAS. We thank Kristina Seiffert-Sinha, M.D. for her critical review of the paper. We thank BKS for continuous guidance and support.

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Please cite this article as: R. Dey-Rao, A.A. Sinha, Genome-wide transcriptional profiling of chronic cutaneous lupus erythematosus (CCLE) peripheral blood identifies systemic alterations relevant to the skin ..., Genomics (2014), http://dx.doi.org/10.1016/j.ygeno.2014.11.004