Microarray to deep sequencing: transcriptome and miRNA profiling

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Immunol Res DOI 10.1007/s12026-015-8672-y
Microarray to deep sequencing: transcriptome and miRNA profiling to elucidate molecular pathways in systemic lupus erythematosus
Geeta Rai1 • Richa Rai1 • Amir Hossein Saeidian1 • Madhukar Rai2

Ó Springer Science+Business Media New York 2015

Abstract Systemic lupus erythematosus (SLE) is an autoimmune disease with diverse clinical manifestations and autoantibody repertoires. The etiology of SLE is multifactorial involving genetic, epigenetic and environmental factors. This complexity leads to poor prognosis, which poses major challenges in the treatment of SLE. Understanding the complex genetic pathways and regulatory mechanisms operative in SLE was feasible by utilizing several highly efficient molecular biological tools during the past few years. In this perspective, DNA microarray technology offered a high-throughput platform in unraveling SLE-associated genes. Additionally, extensive microarray analysis had demonstrated aberrant DNA methylation pattern and differential microRNAs, thus contributing to the knowledge of epigenetic modulators and posttranscriptional regulatory machinery in SLE. It was through the aid of these technologies that interferon signature was identified as an important contributor in SLE pathogenesis along with dysregulation of cytokine-, chemokine- and apoptosis-related genes. The emergence of next-generation sequencing technologies such as RNA sequencing has added new dimensions in understanding the dynamics of the disease processes. Compared with microarrays, deep sequencing has provided higher resolution in gene expression measurement along with identification of different splicing events, noncoding RNAs and novel loci in SLE. The focus, therefore, has now been
& Geeta Rai [email protected]; [email protected]
1 Department of Molecular and Human Genetics, Faculty of Science, Banaras Hindu University, Varanasi 221005, India
2 Department of Medicine, Institute of Medical Science, Banaras Hindu University, Varanasi 221005, India

shifted toward the identification of novel gene loci and their isoforms, and their implication in disease pathogenesis. This advancement in the technology from microarray to deep sequencing has helped in deciphering the molecular pathways involved in pathogenesis of SLE and opens new avenues to develop novel treatment strategies for SLE.
Keywords Systemic lupus erythematosus Á Microarray Á DNA methylation Á microRNA Á RNA sequencing
Systemic lupus erythematosus (SLE) is a chronic autoimmune disease with characteristic presentation of a diverse range of autoantibodies against nuclear and cytoplasmic antigens. These antibodies predominantly target doublestranded DNA (dsDNA) and extractable nuclear antigens (ENA) [1]. They also have varied clinical manifestations affecting multiple organs and organ systems including kidney, heart, skin, joints and nervous system [2]. The multifactorial etiology of SLE includes genetic, epigenetic and environmental factors [3, 4]. Moreover, it also involves cascades of events including increased apoptosis of lymphocytes and excessive neutrophil extracellular trap (NET) formation [5, 6]. Abnormalities in clearance of apoptotic cells and impaired NET degradation contribute to autoantigenic loads in SLE [7–9]. Thus, defects of the innate immune system together with autoimmune responses of T and B lymphocytes result in autoantibody generation and immune complexes, which in turn lead to severe pathology associated with vital organs [10] as pictorially presented in Fig. 1. Nevertheless, much remains to be elucidated about gene expression levels and related regulatory mechanisms in SLE.


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Fig. 1 Pictorial representation of the factors associated with SLE pathogenesis. A hypothetical model is designed to represent the cumulative effect of genetic risk, environmental factors, epigenetic modification and immune function dysregulation that contributes to SLE pathogenesis. Defective clearance of apoptotic debris and NET

degradation activates plasmacytoid dendritic cells that release IFNa. The digested debris displayed on the cell surface of antigen presenting cells (APC) activates Th-1 cells that co-stimulate B cells. B cell hyperactivity leads to autoantibody generation and formation of immune complexes affecting various vital organs

Emergence of microarray technology shifted the trend toward genome-wide expression profiling to identify the gene signatures associated with SLE. It revealed type I interferon (IFN) signatures and several inflammatory, immune responsive, cell cycle-related candidate genes involved in SLE disease pathogenesis [11, 12]. Interestingly, epigenetic modulation via aberrant DNA methylation or histone acetylation also contributes to the activation of immune machinery in SLE. Whole-genome methylation arrays revealed global hypomethylated loci in SLE that are involved in the hematological system and immune cell trafficking [13]. In addition, microRNA (miRNA) has been identified as an important posttranscriptional regulator that controls gene expression by mRNA cleavage or repression of mRNA translation [14, 15]. Differentially expressed miRNAs have been identified in SLE using extensive microarray analysis. However, identification of noncoding RNAs (ncRNAs) that regulate gene expression was beyond the coverage of the microarray approach, but could be achieved through next-generation sequencing. Deep sequencing of transcripts led to identification of novel transcripts, different splicing events and splice variants associated with SLE [16].
This review focuses on evaluating the various technological approaches employed in elucidating the mechanisms involved in the initiation and progression of SLE. Deep sequencing technologies are much more advanced compared to the microarray technique and offer a better tool for comprehensive analysis of the disease progression

mechanism which can further be utilized to design a targetspecific drug therapy for SLE.
Expression profiling of genes in SLE by DNA microarrays
Interferon signatures in SLE
Extensive gene expression profiling studies demonstrated overexpression of type I interferon-regulated genes (also known as IFN signatures) in peripheral blood [17, 18], synovium biopsies [19] and platelets [20] of SLE patients using microarrays. In addition, similar studies on different leukocyte subsets of SLE patients also displayed upregulation of unique IFN signature genes [21]. Elevated expression of IFN genes had been shown to have a positive correlation with the systemic lupus erythematosus disease severity index 2000 (SLEDAI-2000) [22, 23]. Pathway-based meta-analysis of the four independent microarray studies in SLE patients by Arasappan et al. [24] identified 37 meta-signature genes, 12 of which were involved in interferon signaling and were either interferon induced or interferon regulated. These include IFI35 (interferon-inducible protein), IFIT1, IFIT3 (interferon-inducible tetratricopeptide repeats), IFITM1 (interferon-induced transmembrane protein), OAS1 (2050oligoadenylatesythatase 1) and MX1 (myxovirus resistance 1) [24], which had been previously reported [12, 17, 18]. The microarray studies in accordance with the previous reports


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Table 1 Differentially expressed cytokine, chemokines and their receptors in SLE patients

SLE patients Source




Active SLE Inactive SLE




SLE patients

BMMCs B cells T cells Both B cells
and T cells Monocytes






TACE, TNF RII, IL-17. [30] IL-13RA2





TNFAIP6, IL-3, IL-1RN, IL-1R II, IL-18R I, IL-13RA2

IL-2RB, CCR7, IL-10RA [12]







CCR2, IL-6, TNF-a, IFNc, TGF-b






IL-23, IL-17A, IL-17F






CXCL2, CCL3, CCL14, CCL20, IL-1, IL-6, TNF



[25, 26] suggest a strong correlation of the IFN signaling pathway in SLE disease progression. This specific induction of IFN-regulated genes in SLE differentiates it from other autoimmune diseases like antiphospholipid syndrome, multiple sclerosis and rheumatoid arthritis [27–29]. These parameters could be used for diagnosis and monitoring the progression of disease. The molecules involved in the IFN pathway could be promising targets for development of novel therapeutics in SLE.
Other cytokine and chemokine signatures in SLE
Several cytokine-, chemokine- and apoptosis-related genes associated with SLE have been identified using microarray technology. Genes belonging to TNF/death receptor pathway (TNF RII, TRAIL, TRAIL R3 and TRAIL R4), IL-1 cytokine family i.e., IL-1a and IL-1b, and IL-1 RII and IL1R AcP and both IL-8 and its receptors CXCR1 and CXCR2 were observed to be elevated in SLE patients. IL-16, TGFbRIII and chemokine receptor CCR7 were downregulated in SLE patients [11]. In addition, IL-3, IL-1RN, IL-18 R1 and IL-13Ra2 genes were also reported to be differentially expressed in SLE patients [12]. Elevated expression of IL-17 and IL-13Ra2 was observed in active SLE patients as compared to inactive patients [30]. In bone marrow mononuclear cells (BMMCs) of SLE patients, CCR5 and CX3CR1 expressions were downregulated as compared to those of healthy individuals. However, expressions of CCR5, CXCL3L1, CXCL2 and CXCL3 were upregulated in peripheral blood mononuclear cells (PBMCs) as compared to BMMCs in SLE patients [31]. These genes are associated with chemotaxis properties and migration of leukocytes.

Studies of B cell transcriptomes of quiescent SLE patients showed characteristic expression of IL-4 signature genes as compared to other lupus patients and controls [32]. In a study on SLE monocytes by Korman et al. [33], subsets of patients showed chemokine signatures including CXCL2, CCL3, CCL14, CCL20 and proinflammatory cytokines: IL-1, IL-6 and TNF. A meta-analysis approach revealed CCL3, CCR1, IL-1R2, IL-1B, IL-1RN and IL-8 as inflammatory responsive signature genes [24]. Interestingly, studies on leukocyte subsets demonstrated lymphocyte-specific expression of chemokines. CXCR5, which is required for the follicular localization of B cells, was downregulated in SLE B cell. In contrast, CCR2 gene was upregulated in SLE B cells, whereas downregulated in T cells. IL-1RN and CXCL2, associated with inflammatory response, were upregulated in myeloid SLE cells. Among the differentially expressed genes, IL-17A, IL-17F and IL-23 were commonly expressed in B and T cells of SLE patients [21]. Differentially expressed cytokines and chemokines in SLE identified using microarray technology are summarized in Table 1. Taken together, studies targeting each of the leukocyte subsets separately demonstrated specific contributions of B cells, T cells and myeloid cells in the pathogenesis of SLE. This suggests that prolonged cytokine production in SLE might incite inflammation and lead to massive tissue destruction.
Dissecting genes/pathways associated with active and inactive SLE patients
SLE patients have episodes of flare and remission. In an attempt to identify the genes associated with disease severity index and discrimination of active SLE versus SLE in


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Fig. 2 Advancement over a decade in the technologies used to unravel the disease-specific events associated with SLE pathogenesis. Each technique has added new perspectives to identify disease mechanisms and expand our understanding of SLE

remission, a gene profiling approach was employed. Rus et al. [30] identified genes upregulated in active SLE included TNF family molecules (TNF-a converting enzyme; TACE and TNF RII); protease family (TIMP-3 and TIMP4); neurotrophic factors family (NT-4 and NT-3); IL-17 and IL-13Ra2; and the co-stimulatory molecules (CD40 and CD27), while P- and R-cadherin genes from the adhesion molecule family were downregulated in PBMCs of active patients. Bone marrow cells of active SLE patients displayed apoptotic and granulopoiesis gene signatures [31]. SandrinGarcia et al. [34] demonstrated uniquely expressed genes in active SLE patients and in SLE with remission. Among differentially expressed genes, SEMA4A and BIRC1 were induced, whereas ERCC4, PLK4, CDK2AP1, STK17A and

DRG2 were repressed in active SLE. In SLE with remission, LGALS1 and DHX40 were overexpressed, while CDK6 was underexpressed. The dysregulated genes in both the SLE variants were associated with cell death and growth, cell differentiation, metabolism, cell communication, response to stimulus, regulation of cellular processes and regulation of enzyme activity [34]. Identification of the genes associated with the active disease could serve as potential biomarkers for different stages of SLE.
Pathways dysregulated in SLE
Functional characterization of the huge array of genes (differentially expressed) identified by microarray analysis


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Fig. 3 Pictorial representation of epigenetic alterations in promoter region of a gene. DNMT and methyl-CpG-binding domain (MBD) methylate CpG islands (black circles), which leads to transcriptional silencing of genes, whereas inactivation of these enzymes leads to
in SLE patients is necessary to evaluate the canonical function and networks dysregulated in SLE. Functions impacted due to dysregulation of genes were identified as DNA damage/repair pathway that may result in the production of nuclear-targeted autoantibody [28] and cell signal transduction, DNA and RNA processing, extracellular matrix and ion channel and transporter functions [35]. Differentially expressed genes in BMMCs and PBMCs of SLE patients were observed to participate in cell growth and differentiation, cellular movement and morphology, immune response and other hematopoietic cell functions [31]. Arasappan et al. [24] identified dysregulated cellular proliferation and differentiation and protein folding-related genes as meta-signature genes using meta-analysis. Altogether, the microarray analysis has unraveled several immunological pathways associated with the disease. It also dissects out several pathways in the active SLE patients and patients in remission, which could be targeted for potential therapeutic modulation. These studies have provided important insights into genetic pathways underlying SLE (Fig. 2), as well as the effector cells and molecules involved in its pathogenesis.
The abnormalities in B and T cells of SLE patients identified by DNA microarray
The detrimental role of B lymphocytes in SLE is evident from the presence of vast array of autoantibodies against self components [1]. It is the cross talk among B cells and T helper cells which play a crucial role in the breakdown of tolerance to B cells [36, 37]. To understand the contribution of B cells and T cells in SLE pathogenesis, several studies have been conducted. For instance, genome-wide association studies revealed various SLE-susceptible genes including BLK, ETS,

activation of gene. The acetylation of lysine residue (represented with red circle) in histone tail by histone acetyl transferase enzyme (HAT) activates the gene expression (Color figure online)
PTPN22, FCc genes (FCGR2A and FCGR3A), HLA genes, IRF5 and STAT4 [38–40]. These SLE-associated genetic variants profoundly affect B cell signaling pathway [41]. Further, a few studies using microarray technology with focus on each lymphocyte subsets individually elucidated abnormality in immune signaling in SLE [21]. They identified that the differentially expressed genes in B cells were associated with STAT3-regulated pathway. Elevated expression of cell cycle-related transcripts (CCND2) and MKI67 in B cells [21] suggests the presence of actively cycling B cells in periphery of SLE patients [42]. However, T cells showed upregulation of metallothioneins which was speculated to result from cell activation and/or chemotaxis [43]. SGK1 and c-Jun (JUN) transcripts, which are the targets of the p38 MAPK stress kinase pathway, were reported to be upregulated in T cells, and c-Jun is also a target of the antigen-driven Ras/MAPK pathway [21]. Taken together, these studies have indicated the interplay of T cells and B cells along with the intersection of various inflammatory loops and different stages of regulatory events which collectively leads to the pathogenesis of SLE.
Epigenetic alterations in SLE
The etiology of SLE is complex and multifactorial. In recent years, detrimental effect of epigenetic modifications has been evident in SLE [44]. DNA methylation and histone acetylation are the two major epigenetic modulators which contribute in SLE pathogenesis. The DNA methylation usually occurs in the cytosine of CpG dinucleotides (also known as CpG island), which are unevenly distributed throughout the genome. Remarkable percentage of CpG islands are reported to lie in the promoter of proteincoding genes or in repetitive sequences [45]. Methylation


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of CpGs in ‘‘CpG islands’’ of promoter region has functional consequences, like transcriptional silencing of a gene (Fig. 3). Another epigenetic modification occurs in histones that are organized as octamers and constitute DNA nucleosomes, the repeating subunit of chromatin. The alterations in histone include lysine acetylation and methylation and serine phosphorylation. Most importantly, histone acetylation is associated with transcriptional activation of the gene (Fig. 3) [46].
DNA methylation array
Initial studies reported hypomethylation of genes like CD5, CD11a and CD70 in SLE, suggesting methylation to be a key epigenetic event in SLE [47–49]. Hence, DNA methylation arrays emerged as useful tools to identify the methylation pattern of thousands of genes on a single platform. Jeffries et al. [50] studied the methylation status of 27,000 CpG sites in promoter regions of nearly 15,000 genes and identified 236 hypomethylated CpG sites and 105 hypermethylated sites using methylation array platform. This study identified that the majority of genes were hypomethylated, which was consistent with previous reports of global hypomethylation in lupus T cell [51, 52]. The hypomethylated genes that were observed to be associated with SLEDAI scores in lupus patients included MMP9, PDGFRA, BST2, RAB22A, STX1B2, LGALS3BP, DNASE1L1 and PREX1. Some of hypomethylated genes like ADAMTS1, ALX4, CD9, ESRRA, FGF8, HOXA13, HOXD11, MMP9, MSX1, PDGFRA and SOX5 were also associated with connective tissue development, whereas the hypermethylated genes FOLH1 and GGH are involved in folate biosynthesis [50]. Another study by Lin et al. [13] using the same platform revealed 2165 differentially methylated loci in SLE patients which were predominantly involved in cellular movement, hematological system development and function and immune cell trafficking. They also reported demethylation of IL-10 and IL-1R2 cytokine promoters in SLE patients [13]. It was in concordance with the previous finding that showed dysregulation of cytokines due to promoter hypomethylation [53]. Further, extensive analysis of various immune cell types of SLE patients showed differential methylation of 166 CpGs loci in B cells, 97 CpGs in monocytes and 1033 CpGs in T cells. Of these 63 CpGs in T cells, 58 CpGs in B cell and 23 CpGs in monocytes were observed to be hypomethylated near the interferon genes which indicate persistent hypomethylation of interferon genes [54]. This evidence strongly supports the hypersensitivity of SLE patients to interferons. Moreover, the global hypomethylation pattern observed in SLE could be attributed to the dysregulation of methylation enzymes, mainly DNA methyltransferase (DNMT) and methyl-CpG-binding domain (MBD) [51, 52].

Chromatin-immunoprecipitation array (Chip array)
In addition to the DNA methylation, the contribution of other epigenetic modulators cannot be ignored in SLE pathogenesis. Zhang et al. [55] studied the histone acetylation pattern in 11,321 genes in SLE monocytes using chip array. They identified 179 genes with enriched promoter H4 acetylation in SLE patients compared to controls [55]. Further analysis revealed the association of acetylated genes with inflammation networks, macrophage activation, cell proliferation and antiviral immunity functions. This study also documented that expression of many IFN downstream genes is regulated by histone acetylation [55]. Thus, comprehensive analysis of epigenomic patterns has helped to elucidate several regulatory mechanisms operative in SLE (Fig. 2). Targeting the specific regulatory molecules could serve as an epigenetics-based therapy in SLE.
Profiling of microRNAs: posttranscriptional regulators in SLE
miRNAs are endogenous molecules of *20–24 nucleotides that posttranscriptionally regulate gene expression. Association of autoimmunity and miRNAs was evident from the study by Jakymiw et al. [56], which reported that anti-Su autoantibodies from human patients with rheumatic diseases (including SLE) recognize the human Argonaute2 protein (hAgo2). The Argonaute2 antigen is a miRNA-binding protein and critical core enzyme in the RNAi (RNA interference) pathway. It was speculated that interrupted biogenesis of miRNA could also play an important role in disease progression. Analysis of miRNAs in PBMCs of SLE patients has revealed intriguing patterns of miRNA expression unique to SLE. Dai et al. [57] identified seven downregulated and nine upregulated miRNAs in SLE PBMC. Using TaqMan microRNA assays, Tang et al. [58] studied the expression 156 miRNA in SLE patients; 42 out of them were differentially regulated in SLE. Interestingly, miR-146a which is a negative regulator of the type I interferon pathway was downregulated. Expression of 365 miRNA was analyzed in PBMCs of SLE patients by using TaqMan low-density arrays (TLDA), and a 27-miRNA signature was identified in patients with SLE that correlated with disease activity [59]. Recently, a study in SLE PBMCs identified 29 differentially expressed miRNAs that were all downregulated using the microarray-based platform. These miRNAs potentially target genes involved in the development process, transcription regulator activity, ligand binding and several signal transduction pathways, including multiple cancer pathways and Wnt and mitogen-activated


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Table 2 Dysregulated miRNAs in SLE patients
S. Differentially expressed miRNAs in SLE patients no.



1. HMP-PREDICTED-MIR189, HMP-PREDICTED-MIR61, hsa-miR-196a, hsa-miR-17-5p, hsa-miR-409-3p,


HMP-PREDICTED-MIR78, hsa-miR-21, hsa-miR-142-3p, HMP-PREDICTED-MIR141, hsa-miR-383, HMP-

hsa-miR-342, hsa-miR-299-3p, hsa-miR-198 and mmu-miR- PREDICTED-MIR112 and hsa-miR-184 298

2. (Only seven reported)



miR-31, miR-95, miR-99a,

miR-130b, miR-10a, miR-134 and miR-146a

3. hsa-miR-21, hsa-miR-342, hsa-miR-214, hsa-miR-494, hsa- hsa-miR-296, hsa-miR-196a, hsa-miR-17-5p, hsa-miR-383, [59]

miR-198, hsa-miR-155, hsa-miR-25, hsa-miR-106b, hsa- hsa-miR-184, hsa-miR-379, hsa-miR-15a, hsa-miR-16,

miR-373, hsa-miR-324-3p, hsa-miR-544, hsa-miR-148a,

hsa-miR-150, hsa-let-7a, hsa-let-7d, has-let-7 g, hsa-miR-


98, hsa-miR-532

4. NA 5. Anti-dsDNA? subset

hsa-miR-127-3p, hsa-miR-1271-5p, hsa-miR-1301, hsa-miR- [60]

136-5p, hsa-miR-146b-5p, hsa-miR-154-3p, hsa-miR-154-

5p, hsa-miR-181a-2-3p, hsa-miR-31-5p, hsa-miR-337-5p,

hsa-miR-376a-3p, hsa-miR-376b-3p, hsa-miR-376c-3p,

hsa-miR-379-5p, hsa-miR-381-3p, hsa-miR-382-5p, hsa-

miR-409-5p, hsa-miR-410, hsa-miR-421, hsa-miR-431-5p,

hsa-miR-432-5p, hsa-miR-485-3p, hsa-miR-487b, hsa-

miR-493-5p, hsa-miR-495-3p, hsa-miR-539-5p, hsa-miR-

543, hsa-miR-654-3p, hsa-miR-758-3p

Anti-dsDNA? subset


hsa-miR-155, hsa-miR-106b, hsa-miR-146a, hsa-miR-484, hsa-miR-486-3p, hsa-miR-486-5p, hsa-miR-574-3p, hsamiR-183*, hsa-miR-26a-1*, hsa-miR-625*, hsa-miR-30a, hsa-miR-30c, hsa-miR-19a, hsa-miR-106a, hsa-miR-1423p, hsa-miR-26a, hsa-miR-24, hsa-miR-150, hsa-miR-17, hsa-miR-148a, hsa-miR-28-3p, hsa-miR-20a, hsa-miR-210, hsa-miR-374a, hsa-miR-454, hsa-miR-628-5p, hsa-miR636, hsa-miR-144*, hsa-miR-188-5p, hsa-miR-30e, hsamiR-7-1*, hsa-miR-378, hsa-miR-151-3p
Anti-ENA? subset
hsa-miR-493, hsa-miR-939 Anti-dsDNA?ENA? subset
hsa-miR-223, hsa-miR-29a

hsa-let-7d, hsa-miR-493, hsa-miR-939 Anti-ENA? subset:
hsa-miR-140-5p, hsa-miR-15b, hsa-miR-16, hsa-miR-185, hsa-miR-199a-3p, hsa-miR-19a, hsa-miR-19b, hsa-miR223, hsa-miR-324-3p, hsa-miR-376c, hsa-miR-660, hsamiR-183*, hsa-miR-30a, hsa-miR-30d, hsa-miR-28-3p, hsa-let-7e, hsa-let-7 g, hsa-miR-126, hsa-miR-342-3p, hsamiR-145, hsa-miR-146b-5p, hsa-miR-92a, hsa-miR-20b, hsa-miR-133a, hsa-miR-628-3p, hsa-miR-93*, hsa-miR27a*, hsa-miR-425*
Anti-dsDNA?ENA? subset
hsa-miR-409-3p, hsa-miR-223*, hsa-miR-26a-1*, hsa-miR766, hsa-miR-193b, hsa-miR-18a, hsa-miR-125a-5p, hsamiR-320, hsa-miR-140-3p

protein kinase signaling pathways [60]. Further, a study on SLE patient categorized on the basis of distinct autoantibody profiles revealed differential miRNA expression and differential miRNA-mediated regulation prevailing in each subset of SLE patients. Preferential upregulation of miRNAs (47 miRNAs upregulated and 13 downregulated) was observed in patients with anti-dsDNA autoantibodies. These miRNAs potentially target genes involved in multiple cytokine signaling pathways. However, predominant downregulation of miRNAs (38 miRNAs downregulated and 16 upregulated) was observed in SLE patients with autoantibodies against anti-ENA. These miRNAs regulate the cell cycle and cytoskeleton remodeling pathway. The miRNAs downregulated in all SLE subsets target the interferon-related pathways [61]. Thus,

the subgrouping approach used in this study provided a new perspective to understand the complexity and heterogeneity existing in SLE (Fig. 2). The dysregulated miRNAs identified in SLE patients using various approaches are summarized in Table 2.
In addition, miRNAs also modulate epigenetic changes by regulating enzymes involved in methylation processes. Most importantly, miR-29b and miR-126 negatively regulate DNA methyltransferase 1 (DNMT1) expression in T cell [62, 63]. The underlying disease mechanism involves inflammatory processes and immune system dysregulation which is driven by interplay of different genetic and epigenetic regulations leading to defective pathways. The collective effects may be responsible for various manifestations of the disease.


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Next-generation sequencing
Transcriptome dynamics in SLE by RNA sequencing
Recently, emergence of deep sequencing technology has opened a broader perspective in understanding the mechanisms underlying disease progression. Although evidence is accumulating, very few previous studies pointed toward striking alteration in isoforms, differential splicing events, expression of ncRNAs and novel transcripts in SLE. Initially, Stone et al. [64] identified the IRF5 transcript signature distinctly expressed in SLE patients compared to healthy individuals. These transcripts were considered to be SLE risk-associated haplotypes. Further, differential expression of several ncRNAs in SLE monocytes was observed by extensive transcriptome analysis of monocytes. In addition, silencing of several protein-coding genes that were specifically associated with proliferation and adhesion was observed. Identification of globally altered splicing events and novel loci in SLE is intriguing [16]. Altogether, the study by Shi et al. [16] has suggested that systematic alteration in the transcriptome under diseased conditions is linked with differential regulatory mechanisms. Zhao et al. [65] performed integrated analysis of methylation pattern, mRNA expression and miRNA expression in CD4? cells of SLE patients using a deep sequencing approach. The study was designed to compare SLE patients with different clinical manifestations categorized as SLE with only skin involvement, SLE with skin and renal pathology and SLE with skin, renal and joint pathology. A total of 424 genes significantly associated with immunological processes displayed inverse correlations between mRNA expression and promoter methylation. Distinct miRNAs were also dysregulated in different SLE subgroups. Pathway analysis of upregulated targets and downregulated miRNAs revealed perturbation of immune system processes and cell cycle phase. Eight downregulated miRNAs were hypermethylated in SLE T cells, and thirty-six upregulated miRNAs were observed to be located near hypomethylated CpG sites [65]. Thus, integrated analysis of transcriptomics and epigenomics provides better visualization of the regulation of genes and various biological processes at multiple stages of SLE (Fig. 2).
Whole-exome sequencing
Another advantage of deep sequencing technology is the identification of SNPs lying in exonic region which is also named as ‘‘whole-exome sequencing.’’ These SNPs are

associated with functional outcome on gene expression (gain-of-function or loss-of-function mutation). Recently, a study has revealed pathogenic variant in three prime repair exonuclease 1 (TREX1) in SLE pediatric patient by wholeexome sequencing. This is the rare and homozygous mutation in TREX1 gene which leads to the generation of mutant protein TREX1 R97H. It was observed to be associated with reduced exonuclease activity and an elevated IFNa signature in that patient [66]. Further, using whole-exome sequencing, the gain-of-function mutation in IFIH1 (also called as MD5) gene that was previously reported in Aicardi–Goutie`res syndrome [67] has also been observed in early onset and refractory SLE [68]. The mutation in this gene is associated with enhanced binding to RNA, which thus leads to induction of IFN in SLE. This approach could further be used to identify rare or novel deleterious variants as genetic causes of SLE and play an important role in the development of personalized medicine for SLE.
Future perspectives
The diagnosis, prognosis and therapeutics of SLE are more challenging due to the complex nature of the disease. Deep sequencing technology has brought a new revolution in understanding the dynamics of disease processes. This approach in addition to the earlier techniques like DNA microarray, methylation array and miRNA profiling has provided new insights into the regulatory mechanisms prevailing in SLE as summarized in Fig. 2. The identification of various disease-specific events such as different SLE risk-associated isoforms, altered splicing events, detection of noncoding RNAs and novel loci has unraveled various disease processes not known previously. Further, integrative analysis of epigenomics, genomics and transcriptomics has suggested that multistep regulatory processes are operative in SLE. Moreover, studying SLE patients by categorizing on the basis of clinical manifestations or autoantibody profiles will lead to better understanding of the heterogeneity of the disease. Novel molecules identified using these techniques could further be used as unique prognostic markers for the diagnosis of SLE. These could also be useful in designing targeted and specific drugs for the SLE patients with varying disease manifestations and autoantibody repertoires.
Acknowledgments The authors acknowledge and thank the Department of Biotechnology, New Delhi, India (BT/PR4619/MED/ 30/834/2012), for financial assistance to Geeta Rai and the Council of Scientific and Industrial Research (CSIR), New Delhi, India, for a research fellowship to Richa Rai.


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1. Sherer Y, Gorstein A, Fritzler MJ, Shoenfeld Y. Autoantibody explosion in systemic lupus erythematosus: more than 100 different antibodies found in SLE patients. Semin Arthritis Rheum. 2004;34:501–37.
2. Yu C, Gershwin ME, Chang C. Diagnostic criteria for systemic lupus erythematosus: a critical review. J Autoimmun. 2014; 48–49:10–3.
3. Hochberg MC. The epidemiology of systemic lupus erythematosus. In: Wallace DJ, Hahn BH, editors. Dubois’ lupus erythematosus. 7th ed. Baltimore: Williams and Wilkins; 1997. p. 49–65.
4. Danchenko N, Satia JA, Anthony MS. Epidemiology of systemic lupus erythematosus: a comparison of worldwide disease burden. Lupus. 2006;15:308–18.
5. Perniok A, Wedekind F, Herrmann M, Specker C, Schneider M. High levels of circulating early apoptotic peripheral blood mononuclear cells in systemic lupus erythematosus. Lupus. 1998;7:113–8.
6. Lande R, Ganguly D, Facchinetti V, Frasca L, Conrad C, Gregorio J, Meller S, Chamilos G, Sebasigari R, Riccieri V, Bassett R, Amuro H, Fukuhara S, Ito T, Liu YJ, Gilliet M. Neutrophils activate plasmacytoid dendritic cells by releasing self-DNApeptide complexes in systemic lupuserythematosus. Sci Transl Med. 2011;3:73ra19.
7. Ren Y, Tang J, Mok MY, Chan AW, Wu A, Lau CS. Increased apoptotic neutrophils and macrophages and impaired macrophage phagocytic clearance of apoptotic neutrophils in systemic lupus erythematosus. Arthritis Rheum. 2003;48:2888–97.
8. Hakkim A, Fu¨rnrohr BG, Amann K, Laube B, Abed UA, Brinkmann V, Herrmann M, Voll RE, Zychlinsky A. Impairment of neutrophil extracellular trap degradation is associated with lupus nephritis. Proc Natl Acad Sci USA. 2010;107:9813–8.
9. Darrah E, Andrade F. NETs: the missing link between cell death and systemic autoimmune diseases? Front Immunol. 2013;3:428.
10. Shlomchik MJ, Craft JE, Mamula MJ. From T to B and back again: positive feedback in systemic autoimmune disease. Nat Rev Immunol. 2001;1:147–53.
11. Rus V, Atamas SP, Shustova V, Luzina IG, Selaru F, Magder LS, Via CS. Expression of cytokine- and chemokine-related genes in peripheral blood mononuclear cells from lupus patients by cDNA array. Clin Immunol. 2002;102:283–90.
12. Han GM, Chen SL, Shen N, Ye S, Bao CD, Gu YY. Analysis of gene expression profiles in human systemic lupus erythematosus using oligonucleotide microarray. Genes Immun. 2003;4:177–86.
13. Lin SY, Hsieh SC, Lin YC, Lee CN, Tsai MH, Lai LC, Chuang EY, Chen PC, Hung CC, Chen LY, Hsieh WS, Niu DM, Su YN, Ho HN. A whole genome methylation analysis of systemic lupus erythematosus: hypomethylation of the IL10 and IL1R2 promoters is associated with disease activity. Genes Immun. 2012;13:214–20.
14. Zhang S, Semino CE. Design peptide scaffolds for regenerative medicine. Adv Exp Med Biol. 2003;534:147–63.
15. Doench JG, Sharp PA. Specificity of microRNA target selection in translational repression. Genes Dev. 2004;18:504–11.
16. Shi L, Zhang Z, Yu AM, Wang W, Wei Z, Akhter E, Maurer K, Costa Reis P, Song L, Petri M, Sullivan KE. The SLE transcriptome exhibits evidence of chronic endotoxin exposure and has widespread dysregulation of non-coding and coding RNAs. PLoS ONE. 2014;9:e93846.
17. Bennett L, Palucka AK, Arce E, Cantrell V, Borvak J, Banchereau J, Pascual V. Interferon and granulopoiesis signatures in systemic lupus erythematosus blood. J Exp Med. 2003;197:711–23.
18. Ye S, Pang H, Gu YY, Hua J, Chen XG, Bao CD, Wang Y, Zhang W, Qian J, Tsao BP, Hahn BH, Chen SL, Rao ZH, Shen N.

Protein interaction for an interferon-inducible systemic lupus associated gene, IFIT1. Rheumatology. 2003;42:1155–63. 19. Nzeusseu TA, Galant C, Theate I, Maudoux AL, Lories RJ, Houssiau FA, Lauwerys BR. Identification of distinct gene expression profiles in the synovium of patients with systemic lupus erythematosus. Arthritis Rheum. 2007;56:1579–88. 20. Lood C, Amisten S, Gullstrand B, Jo¨nsen A, Allhorn M, Truedsson L, Sturfelt G, Erlinge D, Bengtsson AA. Platelet transcriptional profile and protein expression in patients with systemic lupus erythematosus: up-regulation of the type I interferon system is strongly associated with vascular disease. Blood. 2010;116:1951–7. 21. Becker AM, Dao KH, Han BK, Kornu R, Lakhanpal S, Mobley AB, Li QZ, Lian Y, Wu T, Reimold AM, Olsen NJ, Karp DR, Chowdhury FZ, Farrar JD, Satterthwaite AB, Mohan C, Lipsky PE, Wakeland EK, Davis LS. SLE peripheral blood B cell, T cell and myeloid cell transcriptomes display unique profiles and each subset contributes to the interferon signature. PLoS ONE. 2013;8:e67003. 22. Nikpour M, Dempsey AA, Urowitz MB, Gladman DD, Barnes DA. Association of a gene expression profile from whole blood with disease activity in systemic lupus erythaematosus. Ann Rheum Dis. 2008;67:1069–75. 23. Petri M, Singh S, Tesfasyone H, Dedrick R, Fry K, Lal P, Williams G, Bauer J, Gregersen P, Behrens T, Baechler E. Longitudinal expression of type I interferon responsive genes in systemic lupus erythematosus. Lupus. 2009;18:980–9. 24. Arasappan D, Tong W, Mummaneni P, Fang H, Amur S. Metaanalysis of microarray data using a pathway-based approach identifies a 37-gene expression signature for systemic lupus erythematosus in human peripheral blood mononuclear cells. BMC Med. 2011;9:65. 25. Bengtsson AA, Sturfelt G, Truedsson L, Blomberg J, Alm G, Vallin H, Ronnblom L. Activation of type I interferon system in systemic lupus erythematosus correlates with disease activity but not with antiretroviral antibodies. Lupus. 2000;9:664–71. 26. Ronnblom L, Alm GV. Systemic lupus erythematosus and the type I interferon system. Arthritis Res Ther. 2003;5:68–75. 27. Kirou KA, Lee C, George S, Louca K, Papagiannis IG, Peterson MG, Ly N, Woodward RN, Fry KE, Lau AY, Prentice JG, Wohlgemuth JG, Crow MK. Coordinate overexpression of interferon-alpha-induced genes in systemic lupus erythematosus. Arthritis Rheum. 2004;50:3958–67. 28. Mandel M, Gurevich M, Pauzner R, Kaminski N, Achiron A. Autoimmunity gene expression portrait: specific signature that intersects or differentiates between multiple sclerosis and systemic lupus erythematosus. Clin Exp Immunol. 2004;138:164–70. 29. Perez-Sanchez C, Barbarroja N, Messineo S, Ruiz-Limon P, Rodriguez-Ariza A, Jimenez-Gomez Y, Khamashta MA, Collantes-Estevez E, Cuadrado MJ, Aguirre MA, Lopez-Pedrera C. Gene profiling reveals specific molecular pathways in the pathogenesis of atherosclerosis and cardiovascular disease in antiphospholipid syndrome, systemic lupus erythematosus and antiphospholipid syndrome with lupus. Ann Rheum Dis. 2014;74:1441–9. 30. Rus V, Chen H, Zernetkina V, Magder LS, Mathai S, Hochberg MC, Via CS. Gene expression profiling in peripheral blood mononuclear cells from lupus patients with active and inactive disease. Clin Immunol. 2004;112:231–4. 31. Nakou M, Knowlton N, Frank MB, Bertsias G, Osban J, Sandel CE, Papadaki H, Raptopoulou A, Sidiropoulos P, Kritikos I, Tassiulas I, Centola M, Boumpas DT. Gene expression in systemic lupus erythematosus: bone marrow analysis differentiates active from inactive disease and reveals apoptosis and granulopoiesis signatures. Arthritis Rheum. 2008;58:3541–9.


Immunol Res

32. Garaud JC, Schickel JN, Blaison G, Knapp AM, Dembele D, Ruer-Laventie J, Korganow AS, Martin T, Soulas-Sprauel P, Pasquali JL. B cell signature during inactive systemic lupus is heterogenous: toward a biological dissection of lupus. PLoS ONE. 2011;6:e23900.
33. Korman BD, Huang CC, Skamra C, Wu P, Koessler R, Yao D, Huang QQ, Pearce W, Sutton-Tyrrell K, Kondos G, Edmundowicz D, Pope R, Ramsey-Goldman R. Inflammatory expression profiles in monocyte-to-macrophage differentiation in patients with systemic lupus erythematosus and relationship with atherosclerosis. Arthritis Res Ther. 2014;16:R147.
34. Sandrin-Garcia P, Junta CM, Fachin AL, Mello SS, Baia˜o AM, Rassi DM, Ferreira MC, Trevisan GL, Sakamoto-Hojo ET, Louzada-Ju´nior P, Passos GA, Donadi EA. Shared and unique gene expression in systemic lupus erythematosus depending on disease activity. Ann N Y Acad Sci. 2009;1173:493–500.
35. Wu YS, Fan RQ, Chen DC, Xuan GW. Gene expression profiling of peripheral leukocytes from patients with systemic lupus erythematosus using oligonucleotide DNA microarray. Di Yi Jun Yi Da Xue Xue Bao. 2005;25:929–34.
36. Ferna´ndez-Gutie´rrez B, de Miguel S, Morado C, Herna´ndezGarc´ıa C, Ban˜ares A, Jover JA. Defective early T and T-dependent B cell activation in systemic lupus erythematosus. Lupus. 1998;7:314–22.
37. Kil LP, Hendriks RW. Aberrant B cell selection and activation in systemic lupus erythematosus. Int Rev Immunol. 2013;32:445–70.
38. Graham RR, Hom G, Ortmann W, Behrens TW. Review of recent genome-wide association scans in lupus. J Intern Med. 2009;265: 680–8.
39. Han JW, Zheng HF, Cui Y, Sun LD, Ye DQ, et al. Genome-wide association study in a Chinese Han population identifies nine new susceptibility loci for systemic lupus erythematosus. Nat Genet. 2009;41:1234–7.
40. Yang W, Shen N, Ye D-Q, Liu Q, Zhang Y, et al. Genome-wide association study in Asian populations identifies variants in ETS1 and WDFY4 associated with systemic lupus erythematosus. PLoS Genet. 2010;6:e1000841.
41. Vaughn SE, Kottyan LC, Munroe ME, Harley JB. Genetic susceptibility to lupus: the biological basis of genetic risk found in B cell signaling pathways. J Leukoc Biol. 2012;92:577–91.
42. Lugar PL, Love C, Grammer AC, Dave SS, Lipsky PE. Molecular characterization of circulating plasma cells in patients with active systemic lupus erythematosus. PLoS ONE. 2012;7:e44362.
43. Yin X, Knecht DA, Lynes MA. Metallothionein mediates leukocyte chemotaxis. BMC Immunol. 2005;6:21.
44. Javierre BM, Fernandez AF, Richter J, Al-Shahrour F, MartinSubero JI, Rodriguez-Ubreva J, et al. Changes in the pattern of DNA methylation associate with twin discordance in systemic lupus erythematosus. Genome Res. 2010;20:170–9.
45. Gardiner-Garden M, Frommer M. CpG islands in vertebrate genomes. J Mol Biol. 1987;196:261–82.
46. Ballestar E, Esteller M, Richardson BC. The epigenetic face of systemic lupus erythematosus. J Immunol. 2006;176:7143–7.
47. Oelke K, Lu Q, Richardson D, Wu A, Deng C, Hanash S, Richardson B. Overexpression of CD70 and overstimulation of IgG synthesis by lupus T cells and T cells treated with DNA methylation inhibitors. Arthritis Rheum. 2004;50:1850–60.
48. Garaud S, Le Dantec C, Jousse-Joulin S, Hanrotel-Saliou C, Saraux A, Mageed RA, Youinou P, Renaudineau Y. IL-6 modulates CD5 expression in B cells from patients with lupus by regulating DNA methylation. J Immunol. 2009;182:5623–32.
49. Zhao M, Wu X, Zhang Q, Luo S, Liang G, Su Y, Tan Y, Lu Q. RFX1 regulates CD70 and CD11a expression in lupus T cells by recruiting the histone methyltransferase SUV39H1. Arthritis Res Ther. 2010;12:R227.

50. Jeffries MA, Dozmorov M, Tang Y, Merrill JT, Wren JD, Sawalha AH. Genome-wide DNA methylation patterns in CD4? T cells from patients with systemic lupus erythematosus. Epigenetics. 2011;6:593–601.
51. Balada E, Ordi-Ros J, Serrano-Acedo S, Martinez-Lostao L, Vilardell-Tarre´s M. Transcript overexpression of the MBD2 and MBD4 genes in CD4? T cells from systemic lupus erythematosus patients. J Leukoc Biol. 2007;81:1609–16.
52. Zhu X, Liang J, Li F, Yang Y, Xiang L, Xu J. Analysis of associations between the patterns of global DNA hypomethylation and expression of DNA methyltransferase in patients with systemic lupus erythematosus. Int J Dermatol. 2011;50:697–704.
53. Mi XB, Zeng FQ. Hypomethylation of interleukin-4 and -6 promoters in T cells from systemic lupus erythematosus patients. Acta Pharmacol Sin. 2008;29:105–12.
54. Absher DM, Li X, Waite LL, Gibson A, Roberts K, Edberg J, Chatham WW, Kimberly RP. Genome-wide DNA methylation analysis of systemic lupus erythematosus reveals persistent hypomethylation of interferon genes and compositional changes to CD4? T-cell populations. PLoS Genet. 2013;9:e1003678.
55. Zhang Z, Song L, Maurer K, Petri MA, Sullivan KE. Global H4 acetylation analysis by ChIP-chip in systemic lupus erythematosus monocytes. Genes Immun. 2010;11:124–33.
56. Jakymiw A, Ikeda K, Fritzler MJ, Reeves WH, Satoh M, Chan EK. Autoimmune targeting of key components of RNA interference. Arthritis Res Ther. 2006;8:R87.
57. Dai Y, Huang YS, Tang M, Lv TY, Hu CX, Tan YH, Xu ZM, Yin YB. Microarray analysis of microRNA expression in peripheral blood cells of systemic lupus erythematosus patients. Lupus. 2007;16:939–46.
58. Tang Y, Luo X, Cui H, Ni X, Yuan M, Guo Y, Huang X, Zhou H, de Vries N, Tak PP, Chen S, Shen N. MicroRNA-146A contributes to abnormal activation of the type I interferon pathway in human lupus by targeting the key signaling proteins. Arthritis Rheum. 2009;60:1065–75.
59. Stagakis E, Bertsias G, Verginis P, Nakou M, Hatziapostolou M, Kritikos H, Iliopoulos D, Boumpas DT. Identification of novel microRNA signatures linked to human lupus disease activity and pathogenesis: miR-21 regulates aberrant T cell responses through regulation of PDCD4 expression. Ann Rheum Dis. 2011;70: 1496–506.
60. Liu D, Zhao H, Zhao S, Wang X. MicroRNA expression profiles of peripheral blood mononuclear cells in patients with systemic lupus erythematosus. Acta Histochem. 2014;116:891–7.
61. Chauhan SK, Singh VV, Rai R, Rai M, Rai G. Differential microRNA profile and post-transcriptional regulation exist in systemic lupus erythematosus patients with distinct autoantibody specificities. J Clin Immunol. 2014;34:491–503.
62. Zhao S, Wang Y, Liang Y, Zhao M, Long H, Ding S, Yin H, Lu Q. MicroRNA-126 regulates DNA methylation in CD4? T cells and contributes to systemic lupus erythematosus by targeting DNA methyltransferase 1. Arthritis Rheum. 2011;63:1376–86.
63. Qin H, Zhu X, Liang J, Wu J, Yang Y, Wang S, Shi W, Xu J. MicroRNA-29b contributes to DNA hypomethylation of CD4? T cells in systemic lupus erythematosus by indirectly targeting DNA methyltransferase 1. J Dermatol Sci. 2013;69:61–7.
64. Stone RC, Du P, Feng D, Dhawan K, Ro¨nnblom L, Eloranta ML, Donnelly R, Barnes BJ. RNA-Seq for enrichment and analysis of IRF5 transcript expression in SLE. PLoS ONE. 2013;8:e54487.
65. Zhao M, Liu S, Luo S, Wu H, Tang M, Cheng W, Zhang Q, Zhang P, Yu X, Xia Y, Yi N, Gao F, Wang L, Yung S, Chan TM, Sawalha AH, Richardson B, Gershwin ME, Li N, Lu Q. DNA methylation and mRNA and microRNA expression of SLE CD4? T cells correlate with disease phenotype. J Autoimmun. 2014;54:127–36.


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Microarray to deep sequencing: transcriptome and miRNA profiling