Next Article in Journal
Rare Pathogenic Variants in Genes Implicated in Glutamatergic Neurotransmission Pathway Segregate with Schizophrenia in Pakistani Families
Next Article in Special Issue
Genetic Variants Associated with Neuropeptide Y Autoantibody Levels in Newly Diagnosed Individuals with Type 1 Diabetes
Previous Article in Journal
Genome-Wide Association Mapping for Heat and Drought Adaptive Traits in Pea
Previous Article in Special Issue
Next Generation Sequencing Identifies the HLA-DQA1*03:03 Allele in the Type 1 Diabetes Risk-Associated HLA-DQ8 Serotype
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Genes
Volume 12
Issue 12
10.3390/genes12121898
genes-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Nucleic Acid-Sensing and Interferon-Inducible Pathways Show Differential Methylation in MZ Twins Discordant for Lupus and Overexpression in Independent Lupus Samples: Implications for Pathogenic Mechanism and Drug Targeting

by
Miranda C. Marion
1,2,
Paula S. Ramos
3,
Prathyusha Bachali
4,
Adam C. Labonte
4,
Kip D. Zimmerman
2,
Hannah C. Ainsworth
1,2,
Sarah E. Heuer
4,5,
Robert D. Robl
4,
Michelle D. Catalina
4,
Jennifer A. Kelly
6,
Timothy D. Howard
7,
Peter E. Lipsky
4,
Amrie C. Grammer
4 and
Carl D. Langefeld
1,2,*
1
Department of Biostatistics and Data Science, Division of Public Health Sciences, Wake Forest School of Medicine, Winston-Salem, NC 27157, USA
2
Center for Precision Medicine, Wake Forest School of Medicine, Winston-Salem, NC 27157, USA
3
Division of Rheumatology and Immunology, Department of Medicine, Medical University of South Carolina, Charleston, SC 29425, USA
4
AMPEL BioSolutions, LLC and RILITE Research Institute, Charlottesville, VA 22902, USA
5
The Jackson Laboratory, Tufts Graduate School of Biomedical Sciences, Bar Harbor, ME 04609, USA
6
Arthritis and Clinical Immunology Research Program, Oklahoma Medical Research Foundation, Oklahoma City, OK 73104, USA
7
Department of Biochemistry, Wake Forest School of Medicine, Winston-Salem, NC 27157, USA
*
Author to whom correspondence should be addressed.
Genes 2021, 12(12), 1898; https://doi.org/10.3390/genes12121898
Submission received: 29 October 2021 / Revised: 22 November 2021 / Accepted: 25 November 2021 / Published: 26 November 2021
(This article belongs to the Special Issue Genetics and Epigenetics of Autoimmune Diseases)
Browse Figures
Versions Notes

Abstract

:
Systemic lupus erythematosus (SLE) is a chronic, multisystem, autoimmune inflammatory disease with genomic and non-genomic contributions to risk. We hypothesize that epigenetic factors are a significant contributor to SLE risk and may be informative for identifying pathogenic mechanisms and therapeutic targets. To test this hypothesis while controlling for genetic background, we performed an epigenome-wide analysis of DNA methylation in genomic DNA from whole blood in three pairs of female monozygotic (MZ) twins of European ancestry, discordant for SLE. Results were replicated on the same array in four cell types from a set of four Danish female MZ twin pairs discordant for SLE. Genes implicated by the epigenetic analyses were then evaluated in 10 independent SLE gene expression datasets from the Gene Expression Omnibus (GEO). There were 59 differentially methylated loci between unaffected and affected MZ twins in whole blood, including 11 novel loci. All but two of these loci were hypomethylated in the SLE twins relative to the unaffected twins. The genes harboring these hypomethylated loci exhibited increased expression in multiple independent datasets of SLE patients. This pattern was largely consistent regardless of disease activity, cell type, or renal tissue type. The genes proximal to CpGs exhibiting differential methylation (DM) in the SLE-discordant MZ twins and exhibiting differential expression (DE) in independent SLE GEO cohorts (DM-DE genes) clustered into two pathways: the nucleic acid-sensing pathway and the type I interferon pathway. The DM-DE genes were also informatically queried for potential gene–drug interactions, yielding a list of 41 drugs including a known SLE therapy. The DM-DE genes delineate two important biologic pathways that are not only reflective of the heterogeneity of SLE but may also correlate with distinct IFN responses that depend on the source, type, and location of nucleic acid molecules and the activated receptors in individual patients. Cell- and tissue-specific analyses will be critical to the understanding of genetic factors dysregulating the nucleic acid-sensing and IFN pathways and whether these factors could be appropriate targets for therapeutic intervention.

1. Introduction

Systemic lupus erythematosus (SLE) is a chronic and severe systemic autoimmune disease characterized by the over-production of autoantibodies and heterogeneous clinical manifestations. With more than 100 risk loci identified, a genetic etiology for SLE is unequivocal [1,2,3,4,5,6,7,8]. In fact, the cumulative effect of these risk loci is substantial; the odds ratio (OR) for SLE in individuals of European ancestry is 30 when comparing individuals with the highest 10% of risk allele genetic load (i.e., polygenetic risk score—the weighted count of the number of risk alleles) to individuals in the lowest 10% of genetic load [6]. Despite the strong genetic contribution to risk, the concordance rate between monozygotic (MZ) twins ranges between 24–35%, suggesting that much of the risk remains unexplained and highlighting the potential importance of epigenetic and environmental factors in SLE susceptibility [9].
There is compelling evidence that epigenetic mechanisms, such as 5’ Cytosine methylation, are involved in the pathogenesis of SLE. For example, promoter demethylation at multiple genes in T cells treated with DNA demethylating agents are sufficient to cause lupus in animal models [10]. In recent years, several studies have investigated DNA methylation in SLE patients on a genome-wide scale. The earliest of these genome-wide studies interrogated 27,578 CpG sites in 12 SLE patients and 12 healthy controls using the Illumina Infinium HumanMethylation27 Beadchip, and identified 336 differentially methylated genes, the majority of which were hypomethylated in the cases relative to the controls [11]. Subsequent studies have examined genome-wide methylation in larger samples of SLE patients using the HumanMethylation450 Beadchip (>485,000 CpG sites) in a number of cell types, including naïve CD4+ T cells [12,13,14,15,16], memory and regulatory T cells [17], CD19+ B cells [17], CD14+ monocytes [14,17], granulocytes [14], neutrophils [18], and whole blood or peripheral blood mononuclear cells (PBMC) [19,20,21,22,23,24,25]. Differential methylation has not only been observed when comparing SLE patients to healthy controls, but similar patterns have been identified in SLE patients with nephritis [12,19,22], skin involvement [13], specific antibodies [20], and pediatric SLE [26]. The primary and consistent finding across all these studies has been hypomethylation of interferon-regulated genes across various cell types in cases, regardless of SLE disease activity [27].
The analysis of phenotypically discordant MZ twins represents the ideal design by which to assess the role of epigenetic variation in disease etiology and trait heritability while controlling for genetic background [28] and has revealed the existence of differentially methylated regions associated with several autoimmune diseases, including SLE [29], type 1 diabetes [30], psoriasis [31], and ulcerative colitis [32]. To date, the only previously published twin methylation study in SLE that exclusively used MZ twins quantified DNA methylation in white blood cells from 15 discordant MZ twin pairs at 1505 CpG sites in 807 genes using the Illumina GoldenGate Methylation Cancer Panel I [29]. Here, we performed a genome-wide analysis of DNA methylation in a discovery cohort of MZ twins discordant for SLE. The discovery cohort consisted of three twin pairs of European descent, and methylation was measured in whole blood using Illumina’s HumanMethylation450 Beadchip. The two strongest associated signals were validated using pyrosequencing. Findings from the discovery cohort were replicated in an independent set of MZ twins from Denmark. We then evaluated gene expression data from multiple cell types and kidney biopsies from 10 independent SLE cohorts to identify genes proximal to CpGs exhibiting differential methylation (DM) in the SLE-discordant MZ twins and exhibiting differential expression (DE) in independent SLE GEO cohorts (DM-DE genes) for pathway analyses. Together, the methylation, gene expression, and pathway analyses uncovered two separable yet complimentary molecular pathways of lupus pathogenesis, shedding light on potential drug repositioning opportunities and novel therapeutic targets for SLE.

2. Materials and Methods

2.1. Discovery Cohort

Genomic DNA was extracted from peripheral blood of three female MZ twin pairs of European ancestry discordant for SLE enrolled in the Lupus Family Registry and Repository (LFRR) [33]. All cases met ACR classification criteria for SLE [34].

2.2. Replication Cohort

An SLE study of 15 twin pairs from Denmark, assayed on the HumanMethylation450 Beadchip, in monocytes, CD4+ T cells, CD19+ B cells, and granulocytes, was published in 2018 by Ulff-Moller et al. [14]. These data were downloaded from the Gene Expression Omnibus (GEO, accession no. GSE110607), and all available female MZ twin pairs discordant for SLE were retained for analysis (4 twin pairs). The publication states that of these four female MZ twin pairs discordant for SLE, two of the non-SLE twins had other autoimmune diseases, including Sjogren’s syndrome, systemic sclerosis, autoimmune thyroiditis, and primary biliary cirrhosis. However, this clinical information was not available in GEO.

2.3. Genome-Wide DNA Methylation Assay and Array Validation in LFRR Twins

Genomic DNA (1μg) from each individual was treated with sodium bisulfite using the EZ 96-DNA methylation kit (Zymo Research, Irvine, CA, USA), following the manufacturer’s standard protocol. Genome-wide DNA methylation was assessed using the Illumina Infinium HumanMethylation450 BeadChip (Illumina, Inc., San Diego, CA, USA), which interrogates over 485,500 CpG sites that cover 99% of RefSeq genes (including the promoter, 5’UTR, first exon, gene body, and 3’UTR), as well as 96% of CpG islands and island shores. Arrays were processed using the manufacturer’s standard protocol, with both members of each twin pair being hybridized to the same row on the microarray to minimize batch effects. GenomeStudio software (Illumina, Inc.) was used to perform initial quality control and to calculate the relative methylation level of each interrogated cytosine, which is reported as a β-value given by the ratio of the normalized signal from the methylated probe to the sum of the normalized signals of the methylated and unmethylated probes. This β-value for each CpG site ranges from 0 (unmethylated) to 1 (fully methylated). CpG loci with a stringent detection p-value > 1.0 × 10−5 in any of the samples were excluded (n = 2118 probes) to control for poor-quality assays. Validation of the array data in the LFRR twins was performed by pyrosequencing two of the most significant CpGs probes: cg13304609 (in IFI44L) and cg23570810 (in IFITM1). The correlations between the methylation proportions from the array and pyrosequencing for these two probes were r2 = 0.98 and r2 = 0.99, respectively.

2.4. Collection of Gene Expression Experiments from SLE Patient Datasets

Raw data were downloaded from 10 publicly available gene expression datasets (Supplemental Table S1). Only datasets from female lupus patients were analyzed. Active SLE was defined as a Systemic Lupus Erythematosus Disease Activity Index (SLEDAI) > 6 [35]. This has become the standard threshold for disease activity in recent clinical trials of SLE.

2.5. Data Analysis

To identify differentially methylated genes between unaffected and SLE-affected twins, a paired t-test on the probe-specific β-values was computed separately for the discovery and replication twin datasets. For the discovery set, CpG sites meeting (1) the Benjamini–Hochberg False Discovery Rate (FDR) [36] threshold PFDR < 0.05 (equivalent to p < 1.06 × 10−7) and (2) a mean DNA methylation difference of (Δβ) > |0.085| were considered statistically significant; the mean methylation difference threshold was obtained by maximizing the area under the receiver operator characteristic curve (AUC) as a function of the β-value (described below). The genes related to the differentially methylated CpG sites (as annotated by Illumina for the HumanMethylation450) were queried in the Interferome online database to identify interferon-regulated genes [37]. In addition, significant CpG sites were investigated for evidence of association between DNA methylation pattern and gene expression (mQTL) using the iMETHYL genome browser [38]. These results are based on 100 healthy subjects with RNA-seq data and DNA methylation data in CD4T cells, monocytes, and PBMC.
Statistical analysis of the expression data was completed using the following R packages available from Bioconductor: GEOquery, affy, affycoretools, simpleaffy, gcrma, LIMMA, and GSVA. Non-normalized arrays were first inspected for visual artifacts and poor RNA hybridization using Affymetrix QC plots. Principal component (PC) plots were generated for all cell types in each experiment to identify outliers. After removing outliers, the datasets were normalized using the gcrma package (available in Bioconductor [39], www.bioconductor.org) resulting in log2 intensity values for the R expression set objects (denoted E-sets); an E-set combines several information types in a single structured object: an expression value matrix, phenotypic metadata corresponding to individual samples (phenoData), annotation data describing each feature (probeset) of a microarray platform (featureData), as well as other separate metadata matrices describing the experimental protocol and array platform design. To increase the probability of identifying differentially expressed genes (DE genes), the analyses were completed using normalized datasets prepared using both the native Affymetrix chip definition file (CDF), as well as custom BrainArray Entrez CDFs. Illumina CDFs were used for GSE49454.
The CDF-annotated E-sets were filtered to remove probes with very low intensity values by computing the mean log2 values for each probe across all samples and removing those in the lower half of the range of mean values from the expression set (E-set). Probes missing gene annotation data were also discarded. GCRMA normalized expression values were variance-corrected using local empirical Bayesian shrinkage before calculation of differential expression using the ebayes function in the Bioconductor limma package [40]. The resulting p-values were adjusted for multiple hypothesis testing using Benjamini–Hochberg False Discovery Rate (FDR) [36]. Significant Affymetrix and BrainArray probes within each study were merged and filtered to retain DE probes with a PFDR < 0.2. This list was filtered to retain only the most significant probe per gene.
To identify DM-DE genes, we used a logistic regression model (expression fold change as a binary outcome > 0 versus < 0) to determine cell-type specific thresholds for the difference in the β-value that maximized the area under the ROC curve (AUC) predicting increased differential expression (Figure 1A, Supplemental Figure S1). These thresholds were determined by calculating the area under the receiver operating characteristic curve (AUC) across points at regular intervals between 0 and −0.15 and selecting the values that maximized the AUC. Primary inferences are based on thresholds, which included a logFC in expression > 0 and a mean difference in β < −0.085, −0.055, −0.08, and −0.055 in whole blood, monocytes, B cells, and T cells, respectively. Figure 1A displays these thresholds as vertical bars. For clarity, genes with differential methylation p-values greater than 0.0001 and a mean DNA methylation difference of (Δβ) > |0.025| have been removed from Figure 1A.
The DM-DE genes were analyzed in a pathway analysis using the MCODE [41] clustering algorithm and STRING networking scores [42].
Protein–drug interaction networks were generated for each DM-DE gene individually via STITCH [43], Ingenuity Pathway Analysis (IPA) (Qiagen Bioinformatics: ingenuity.com), and the Drug–Gene Interaction database [44]. Drugs were denoted as (1) known utility in lupus therapy, (2) FDA-approved compound, (3) currently involved in a clinical trial (not necessarily SLE), and (4) generally regarded as safe (GRAS) compounds. Using a hypothesis-driven ranking of the therapeutic potential for SLE applications of specific drugs or compounds, the combined lupus treatment scoring (CoLTS) scores (range −16 to +11) were calculated [45].

3. Results

3.1. Characteristics of the MZ Twins

The LFRR MZ twins were all females of European ancestry, and the SLE-diagnosed twins exhibited a range of SLE clinical conditions (Supplemental Table S2). The Danish MZ twins were also all females of European ancestry. Clinical characteristics such as number of ACR criteria, SLEDAI score, autoantibodies, and medications are described in Ulff-Moller et. al., but were not available in GEO [14].

3.2. Identification of Differentially Methylated Regions in Twins Discordant for SLE

Of the 485,577 CpG sites passing quality control metrics, 59 sites in 33 genes met both a PFDR < 0.05 (equivalent to a non-FDR p < 1.06 × 10−7) and a mean DNA methylation difference of (Δβ) > |0.085| (Table 1). Only two of these significant CpG sites showed increased methylation in the affected twins (hypermethylation), while the remaining 57 exhibited lower methylation (hypomethylation). Of the 33 genes represented in Table 1, 22 are regulated at some level by type I interferons (as defined by Interferome [37]). Eleven genes are novel to our study and have not been previously reported as SLE-related in a genome-wide methylation study, five of which are unrelated to the typical interferon signature (LY6G5C, CXCR1, ATOH8, CACNA1D, MECOM). Lymphocyte antigen 6 complex, locus G5C (LY6G5C), is located within the major histocompatibility complex class III region and codes for a protein associated with the cell membrane by a glycosylphosphatidylinositol linkage and involved in signal transduction [46]. Chemokine (C-X-C motif) receptor 1 (CXCR1) encodes for a protein that is a receptor for interleukin 8. Genetic and expression variation in CXCR1 have been correlated with infections (e.g., active tuberculosis, hepatitis B, Candida albicans) and modestly with SLE [6,47,48,49,50]. Atonal bHLH transcription factor 8 (ATOH8), calcium voltage-gated channel subunit alpha1 D (CACNA1D), and MDS1, and EVI1 complex locus (MECOM) do not have known links to autoimmune disease or infections. Given the gender bias in SLE, it is interesting to note that none of the differentially methylated probes meeting our significance criteria were located on the X chromosome.
We next examined the 59 differentially methylated CpGs from the discovery cohort (Table 1) in the Danish twin replication cohort. Even with the probable dampening effect generated by two of the Danish non-SLE twins having other autoimmune diseases, we observed very high concordance in the direction of the Δβ values. Specifically, 55 (93%), 54 (92%), 52 (88%), and 54 (92%) of the 59 differentially methylated CpG sites in the LFRR twins were concordant in the Danish twins’ monocytes, CD4+ T cells, CD19+ B cells, and granulocytes, respectively. Furthermore, 35, 26, 32, and 33 of the 59 CpG sites were statistically significant (p-value < 0.05) and directionally concordant in the monocyte, CD4+ T cell, CD19+ B cell, and granulocyte expression datasets, respectively; only one of these was statistically significant in the opposite direction (p-value < 0.05; Additional File 1). Thus, the Danish twin data strongly corroborated the global pattern of methylation observed in the LFRR twin data.
Table
Table 1. Differentially methylated probes from three monozygotic twin pairs discordant for SLE.
Table 1. Differentially methylated probes from three monozygotic twin pairs discordant for SLE.
CpG *ChrPos (bp) †GeneΔβp-ValueInterferon-Regulated ‡Relation to CpG ††
Pair1Pair2Pair3Mean
cg13304609179085162IFI44L−0.24−0.27−0.37−0.291.58 × 10−14IRG
cg06872964179085250IFI44L −0.26−0.21−0.241.05 × 10−71IRG
cg03607951179085586IFI44L−0.27−0.3−0.21−0.267.23 × 10−22IRG
cg175153471159047163AIM2−0.09−0.11−0.07−0.093.01 × 10−12IRG
cg082722681200380059ZNF281−0.08−0.07−0.11−0.094.33 × 10−15 S_Shore
cg0102814227004578CMPK2−0.22−0.36−0.43−0.337.98 × 10−8IRGN_Shore
cg1095965127018020RSAD2−0.13−0.1−0.16−0.133.14 × 10−14IRG
cg1054998627018153RSAD2−0.08−0.09−0.1−0.091.95 × 10−91IRG
cg14126601237384708EIF2AK2−0.08−0.1−0.12−0.15.55 × 10−16IRGS_Shore
cg26337070285999873ATOH8−0.06−0.12−0.11−0.17.55 × 10−9
cg047814942202047246CASP10−0.07−0.13−0.08−0.098.39 × 10−8IRG
cg157681382219030752CXCR1−0.09−0.12−0.11−0.117.38 × 10−27
cg13411554353700276CACNA1D−0.06−0.12−0.09−0.098.66 × 10−8
cg229308083122281881PARP9-DTX3L−0.36−0.34−0.4−0.376.74 × 10−126IRGN_Shore
cg081226523122281939PARP9-DTX3L−0.34−0.31−0.51−0.381.11 × 10−9IRGN_Shore
cg009592593122281975PARP9-DTX3L−0.37−0.3−0.34−0.341.32 × 10−56IRGN_Shore
cg069813093146260954PLSCR1−0.24−0.28−0.21−0.246.41 × 10−31IRGN_Shore
cg025563933168866705MECOM−0.08−0.09−0.1−0.093.14 × 10−95 N_Shore
cg07809027415007205CPEB2−0.07−0.1−0.12−0.12.08 × 10−14 S_Shore
cg02215171489379156HERC5−0.08−0.09−0.11−0.094.48 × 10−18IRGS_Shore
cg177862554108814389SGMS2−0.07−0.09−0.11−0.092.01 × 10−16IRG
cg218735244190942744 −0.1−0.1−0.12−0.111.03 × 10−55 Island
cg247406325134486678 −0.11−0.12−0.14−0.122.26 × 10−60
cg06012695628770593 −0.1−0.13−0.113.59 × 10−16
cg25138053631368016 −0.11−0.09−0.07−0.093.67 × 10−15 S_Shore
cg22708150631649619LY6G5C−0.12−0.14−0.17−0.141.05 × 10−19 N_Shore
cg072927736156718177 0.070.10.110.12.22 × 10−17 Island
cg120137137139760671PARP12−0.12−0.14−0.09−0.121.44 × 10−16IRGN_Shore
cg20190772848572496KIAA0146−0.08−0.07−0.13−0.091.40 × 10−8
cg14864167866751182PDE7A−0.25−0.35−0.45−0.351.21 × 10−9 N_Shelf
cg06102678881491328 −0.08−0.12−0.07−0.091.00 × 10−8 Island
cg121104378144098888LY6E−0.16−0.17−0.27−0.23.14 × 10−9IRGN_Shore
cg175558061074448117 −0.08−0.12−0.07−0.091.51 × 10−8 N_Shelf
cg023143391091020653 −0.08−0.14−0.11−0.111.72 × 10−8
cg061880831091093005IFIT3−0.29−0.16−0.31−0.256.18 × 10−8IRG
cg055528741091153143IFIT1−0.2−0.28−0.3−0.266.01 × 10−16IRG
cg1491017510131840954 −0.07−0.11−0.08−0.091.56 × 10−11 N_Shelf
cg1055252311313478IFITM1−0.14−0.12−0.14−0.135.90 × 10−115IRGN_Shelf
cg2056689711313527IFITM1−0.11−0.11−0.09−0.17.00 × 10−62IRGN_Shelf
cg2357081011315102IFITM1−0.24−0.25−0.34−0.271.43 × 10−18IRGN_Shore
cg0303826211315262IFITM1−0.24−0.22−0.29−0.254.41 × 10−40IRGN_Shore
cg2004532011319555 −0.19−0.13−0.2−0.184.85 × 10−17 S_Shore
cg1799036511319718IFITM3−0.16−0.15−0.15−0.168.78 × 10−295IRGS_Shore
cg0892625311614761IRF7−0.15−0.14−0.23−0.172.01 × 10−9IRGIsland
cg12461141115710654TRIM22−0.1−0.08−0.12−0.16.35 × 10−25IRG
cg23571857176658898XAF1−0.07−0.13−0.11−0.11.46 × 10−8IRG
cg049275371776976091LGALS3BP−0.14−0.11−0.2−0.152.77 × 10−10IRG
cg251786831776976267LGALS3BP−0.15−0.11−0.21−0.162.01 × 10−8IRG
cg165037971819476805 −0.08−0.12−0.08−0.095.39 × 10−12 N_Shore
cg158710861856526595 −0.07−0.11−0.08−0.092.08 × 10−11 N_Shelf
cg233520302062198469PRIC2850.130.190.110.142.36 × 10−11 Island
cg167850772142791867MX1−0.11−0.09−0.12−0.118.45 × 10−27IRGN_Shore
cg228620032142797588MX1−0.31−0.25−0.35−0.311.62 × 10−25IRGN_Shore
cg263129512142797847MX1−0.26−0.17−0.2−0.216.28 × 10−15IRGN_Shore
cg215492852142799141MX1−0.5−0.35−0.57−0.476.59 × 10−13IRGS_Shore
cg055438642224979755GGT1−0.08−0.08−0.1−0.091.44 × 10−45
cg200980152250971140ODF3B−0.19−0.22−0.21−0.219.88 × 10−83IRGS_Shore
cg055236032250973101 −0.17−0.23−0.27−0.225.51 × 10−14 S_Shelf
cg022478632250983415 −0.07−0.1−0.11−0.092.51 × 10−13 N_Shore
* CpGs meeting the PFDR < 0.05 threshold (equivalent to p < 1.06 × 10−7) and having |Δβ| > 0.085. † Positions are from Build 37. ‡ IRG as defined by Interferome [37]. †† Island: CpG sites > 200 bp, with GC content > 55% and observed to expected ratio > 0.6. N_shore: 0–2 kb upstream from island; S-shore 0–2 kb downstream from island; N_shelf 2–4 kb upstream from island; S_shelf 2–4 kb downstream from island.
We also sought to determine if the dominating presence of the interferon signature might have masked more modest signals from other individual (non-IFN) loci. After regressing out the mean β-value (methylation value) for the most significant CpG site in each interferon-regulated gene in Table 1 (as defined by Interferome [37]), no additional CpG sites across the genome met an FDR threshold of significance (PFDR > 0.05).
We considered the genomic context of the CpG sites showing aberrant methylation in the LFRR MZ twins. Here, a CpG island was defined as a cluster of CpG sites of greater than 200 bp, with GC content >55%, and the observed-to-expected (under mathematical independence of the Gs and Cs) ratio >0.6 [51]. Interestingly, out of 59 CpG sites differentially methylated, the majority (54%, n = 32) were located in a CpG shore (0–2 kb from island) or shelf (2–4 kb from island), whereas only 8% (n = 5) were located in a CpG island (Table 1). This is in contrast to the composition of the 450k chip in which about one third of the CpG sites reside in islands (Supplemental Figure S2). Notably, the only two hypermethylated CpG sites (relative to the unaffected twin) meeting our significance thresholds reside in CpG islands.

3.3. Hypomethylated Genes Are Overexpressed in Independent Cohorts

Methylation at CpG sites influences gene expression. Thus, linking differential methylation to changes in gene expression by showing that the same genes were associated with SLE in both types of experiments (even in independent samples) would provide further evidence of the importance of these genes and could identify potential actionable mechanisms.
Genes harboring a CpG site with Δβ < −0.085 and p < 0.01 (for differential methylation) were tested for differential expression in whole blood from two independent cohorts, each comparing SLE patients to healthy controls (GSE39088 and GSE49454) (Table 2). Relative to controls, overexpression was observed in both active and inactive SLE patients within almost all of these genes, and the level of expression was highly correlated within the gene expression experiments (experiment 1, r = 0.95; experiment 2, r = 0.99). IFI44L, RADS2, and IFIT1 showed the highest fold changes and comparable increases in expression in active and inactive SLE patients; IFI44L is noteworthy as it has been reported to be predictive of SLE status relative to healthy controls and other autoimmune diseases [52]. Cohorts with expression data derived from monocytes (GSE38351), CD19+, and CD20+ B cells (GSE10325, GSE4588), and CD4+ T cells (GSE10325, GSE51997) reflected a consistent pattern of increased expression in genes meeting the mean (methylation) Δβ threshold of −0.085 (Figure 1B). Upon extending Δβ to <−0.055, the statistically appropriate threshold for detecting differential expression in monocytes and T cells in our dataset (see Methods), an additional 54 hypomethylated genes were evaluated in the gene expression datasets (Supplemental Table S3). Overall, the pattern of differential expression of hypomethylated genes was very similar across the cell subtypes examined (Figure 1B, Supplemental Table S3). Thus, the differential expression results in independent cohorts in multiple cell types provide a multi-omic, independent pseudo-replication, and translational interpretation of the methylation results (Table 2).
Hierarchical clustering (Euclidean distance, complete linkage) of the DM-DE genes using the log fold change (LFC) identified a cluster of nine genes with markedly higher LFC (Figure 1B). This cluster shows a consistent pattern across whole blood, monocytes, B cells, and T cells, as well as in both active and inactive SLE disease. In fact, the LFC remained largely consistent between active and inactive disease across all DM-DE genes. Exceptions to this pattern include FK506 binding protein 5 (FKBP5), parvin beta (PARVB), and strawberry notch homolog 2 (SBNO2) in whole blood, where there is upregulation in active patients and non-significant change in inactive patients. This pattern was not replicated in any of the individual cell types.
Table
Table 2. Differential expression of hypomethylated genes in whole blood from two independent SLE cohorts.
Table 2. Differential expression of hypomethylated genes in whole blood from two independent SLE cohorts.
Active SLE §Inactive SLE §
CpG *ChrPos (bp) †GeneMean ΔβMethylation p-ValueInterferon-Regulated ‡Log FC Expt 1Log FC Expt 2Log FC Expt 1Log FC Expt 2
cg165260471949893ISG15−0.111.28 × 10−4IRG3.12.772.742.59
cg05696877179088769IFI44L−0.36.60 × 10−6IRG3.983.83.643.4
cg01079652179118191IFI44−0.345.34 × 10−4IRG3.542.533.72.33
cg175153471159047163AIM2−0.093.01 × 10−12IRG1.390.861.080.49
cg0102814227004578CMPK2−0.337.98 × 10−8IRG2.761.52.431.51
cg1095965127018020RSAD2−0.133.14 × 10−14IRG4.043.323.763.04
cg14126601237384708EIF2AK2−0.15.55 × 10−16IRG1.472.021.081.68
cg157681382219030752CXCR1−0.117.38 × 10−27 0.430.960.380.66
cg081226523122281939PARP9-DTX3L−0.381.11 × 10−9IRG1.361.561.071.55
cg069813093146260954PLSCR1−0.246.41 × 10−31IRG1.771.251.381.07
cg026946203172109284FNDC3B−0.113.80 × 10−3 0.570.820.410.52
cg150653403195632915TNK2−0.164.04 × 10−3 0.220.310.20.25
cg07809027415007205CPEB2−0.12.08 × 10−14 0.660.520.420.45
cg02215171489379156HERC5−0.094.48 × 10−18IRG2.622.482.142.36
cg058831284169239131DDX60−0.252.13 × 10−5IRG1.241.381.061.46
cg08099136632811251PSMB8−0.111.43 × 10−4IRG−0.39−0.13NSNS
cg00052684635694245FKBP5−0.161.65 × 10−3 1.110.71NSNS
cg059949747139761087PARP12−0.156.89 × 10−5IRG1.521.571.141.25
cg14864167866751182PDE7A−0.351.21 × 10−9 −1.24−0.41−0.82−0.23
cg121104378144098888LY6E−0.23.14 × 10−9IRG 2.661.922.431.7
cg03848588932525008DDX58−0.14.34 × 10−4IRG1.481.31.321.07
cg061880831091093005IFIT3−0.256.18 × 10−8IRG2.253.152.32.87
cg055528741091153143IFIT1−0.266.01 × 10−16IRG3.392.943.422.81
cg2357081011315102IFITM1−0.271.43 × 10−18IRG11.031.030.81
cg1799036511319718IFITM3−0.168.78 × 10−295IRG0.922.230.712.13
cg0892625311614761IRF7−0.172.01 × 10−9IRG1.841.791.41.37
cg08577913114415193TRIM21−0.11.74 × 10−3IRG0.560.930.280.75
cg12461141115710654TRIM22−0.16.35 × 10−25IRG1.1410.991.05
cg2681170511118781408BCL9L−0.091.64 × 10−3 −0.6−0.35−0.41−0.32
cg193477901281332050LIN7A−0.091.87 × 10−4 0.930.991.240.61
cg2580016612113375896OAS3−0.135.36 × 10−5IRG2.522.690.732.35
cg1937165212113415883OAS2−0.112.24 × 10−5IRG1.481.561.641.53
cg037531911343566902EPSTI1−0.19.23 × 10−5IRG2.652.262.712.02
cg002469691399159656STK24−0.116.26 × 10−6 0.810.320.660.36
cg078394571657023022NLRC5−0.236.10 × 10−6IRG0.70.230.530.27
cg23571857176658898XAF1−0.11.46 × 10−8IRG2.851.962.351.68
cg233789411764361956PRKCA−0.116.89 × 10−5IRG−1.11−0.3NSNS
cg251786831776976267LGALS3BP−0.162.0 × 10−8IRG1.161.210.721.05
cg07573872191126342SBNO2−0.152.77 × 10−3IRG0.380.58NSNS
cg078393131917514600BST2−0.123.48 × 10−3IRG1.240.491.170.41
cg215492852142799141MX1−0.476.59 × 10−13IRG2.1221.861.79
cg194605082244422195PARVB−0.11.64 × 10−3 0.540.39NSNS
cg200980152250971140ODF3B−0.219.88 × 10−83IRG1.610.611.360.47
Differential gene expression values come from GSE39088 (Expt 1) and GSE49454 (Expt 2) in whole blood of lupus patients compared with controls. * CpGs with p < 0.01 and |Δβ| > 0.085. † Positions are from Build 37. ‡ As defined by Interferome [37]. § Active disease is defined as ≥6 on the Systemic Lupus Erythematosus Disease Activity Index (SLEDAI) [35].
Although only one of the three affected MZ twins in the discovery cohort had renal involvement, almost all of the genes mapping to differentially methylated CpG sites showed overexpression in both the kidney glomerulus and tubulointerstitium from independent lupus nephritis patients (Table 3). In the glomerulus, 28 genes were overexpressed, 2 were under expressed, and 14 were not significantly differentially expressed in lupus nephritis samples compared to healthy controls. In the tubulointerstitium, 27 were overexpressed, 5 under expressed, and 12 not significantly differentially expressed. IFI44L, MX1, and IFI44 showed the highest levels of overexpression across the two tissues. The fold change was correlated between the two tissues (r = 0.66, p < 0.0001).
Significant DNA methylation sites were further investigated for evidence of association between DNA methylation at a specific CpG site and gene expression (eQTM) using the iMETHYL genome browser with data on 100 healthy Japanese subjects with RNA-seq data and DNA methylation data in CD4T cells, monocytes, and PBMC [38] (Supplemental Table S4). Most of the CpGs from Table 1 that are identified in iMETHYL are eQTMs for the gene in which they reside. In contrast, some are eQTMs for additional genes of interest. For example, cg17515347 is in physical proximity to AIM1, which has an important role in T cell regulation in autoimmune diseases. However, this CpG site is also an eQTM for five other genes in CD4+ T cells (TAGLN2, SLAMF8, DUSP23, PHYIN1 FCRL6), several of which have established autoimmune disease connections. Transgelin-2 may help regulate activation and migration of B cells in lymph node follicles, exhibits increased expression in B cells from lymph nodes in SLE patients, and appears important in host defense [53,54]. SLAM family member 8 (SLAMF8) is a member of the SLAM family of genes of which several members have been associated with multiple autoimmune diseases [55]. FcR-like 6 (FCRL6), a receptor that binds to major histocompatibility complex (MHC) class II HLA-DR, is expressed in B cells and has a tyrosine-based immunoregulatory function [56,57]. Dual-specificity protein phosphate 23 (DUSP23) expression is reportedly higher in CD4+ T cells from SLE patients compared to healthy controls [58]. Thus, DNA methylation in these regions, and potentially others, may have a complex and multifaceted impact on autoimmunity. Annotation of cg20098015 on chromosome 22 is linked to Outer Dense Fiber of Sperm Tails 3 (ODF3B). However, this CpG is an eQTM for SCO2 homolog, mitochondrial and SCO cytochrome oxidase deficient homolog 2 (SCO2), and thymidine phosphorylase (TYMP), both involved in mitochondrial functions.

3.4. Pathway Analysis of DM-DE Genes

Pathway, clustering, and networking analyses were completed to elucidate patterns among the DM-DE genes. Ingenuity Pathway Analysis (IPA) identified two primary canonical pathways: (1) interferon signaling and (2) pattern recognition receptor (PRR) (Figure 2A). The overlap p-value, which tests for independence between known targets of each transcription regulator in a pathway and the list of genes provided, shows very strong association for these two pathways. Other significant pathways of note include the activation of interferon regulatory factors (IRFs) by pattern recognition receptors, retinoic acid-inducible gene I protein (RIG-I)-like receptors in innate immunity, and NF-κB activation by viruses. Figure 2B illustrates the IFN signaling pathway determined by IPA. Notably, in this pathway all of the DM-DE genes are downstream, and none were identified as upstream signaling molecules. IPA also identified 39 upstream regulators (|Z-score| ≥ 2) of the DM-DE genes that showed differential expression between SLE cases and controls in whole blood (Figure 2C).
Table
Table 3. Differential expression of hypomethylated genes in kidney biopsies from independent SLE patients with lupus nephritis.
Table 3. Differential expression of hypomethylated genes in kidney biopsies from independent SLE patients with lupus nephritis.
CpG *ChrPos (bp) †GeneMean ΔβMethylation p-ValueInterferon-Regulated ‡Log FC GlomerulusLog FC Tubulointerstitium
cg165260471949893ISG15 ‖−0.111.28 × 10−4IRG3.324.7
cg05696877179088769IFI44L Δ−0.36.60 × 10−6IRG5.145.94
cg01079652179118191IFI44 ‖−0.345.34 × 10−4IRG3.944.76
cg175153471159047163AIM2−0.093.01 × 10−12IRG0.58NS
cg0102814227004578CMPK2 Δ−0.337.98 × 10−8IRGNSNS
cg1095965127018020RSAD2−0.133.14 × 10−14IRG4.313.36
cg14126601237384708EIF2AK2 Δ−0.15.55 × 10−16IRG1.541.72
cg157681382219030752CXCR1−0.117.38 × 10−27 0.68−0.17
cg081226523122281939PARP9-DTX3L Δ−0.381.11 × 10−9IRGNSNS
cg069813093146260954PLSCR1 Δ−0.246.41 × 10−31IRG1.922.07
cg026946203172109284FNDC3B−0.113.80 × 10−3 NS0.47
cg150653403195632915TNK2 ‖−0.164.04 × 10−3 0.38−0.4
cg07809027415007205CPEB2−0.12.08 × 10−14 NSNS
cg02215171489379156HERC5 −0.094.48 × 10−18IRG3.161.96
cg058831284169239131DDX60−0.252.13 × 10−5IRG1.112.31
cg08099136632811251PSMB8−0.111.43 × 10−4IRG0.762.51
cg00052684635694245FKBP5 §−0.161.65 × 10−3 −1.27−2.77
cg059949747139761087PARP12 ‖−0.156.89 × 10−5IRG2.261.86
cg14864167866751182PDE7A−0.351.21 × 10−9 NSNS
cg121104378144098888LY6E ◊−0.23.14 × 10−9IRG 1.281.23
cg03848588932525008DDX58 ◊−0.14.34 × 10−4IRG2.892.59
cg061880831091093005IFIT3 ◊−0.256.18 × 10−8IRG2.593.14
cg055528741091153143IFIT1 ◊−0.266.01 × 10−16IRG2.242.77
cg2357081011315102IFITM1−0.271.43 × 10−18IRG2.243.29
cg1799036511319718IFITM3−0.168.78 × 10−295IRG2.242
cg0892625311614761IRF7 ‖−0.172.01 × 10−9IRG2.81
cg08577913114415193TRIM21−0.11.74 × 10−3IRG1.350.77
cg12461141115710654TRIM22−0.16.35 × 10−25IRG1.732.86
cg2681170511118781408BCL9L−0.091.64 × 10−3 NSNS
cg193477901281332050LIN7A−0.091.87 × 10−4 NS−0.57
cg2580016612113375896OAS3−0.135.36 × 10−5IRG3.771.1
cg1937165212113415883OAS2−0.112.24 × 10−5IRG4.861.74
cg037531911343566902EPSTI1 −0.19.23 × 10−5IRGNSNS
cg002469691399159656STK24−0.116.26 × 10−6 NS0.28
cg078394571657023022NLRC5−0.236.10 × 10−6IRGNSNS
cg23571857176658898XAF1−0.11.46 × 10−8IRG3.143.05
cg233789411764361956PRKCA−0.116.89 × 10−5IRG−0.48−0.08
cg251786831776976267LGALS3BP−0.162.0 × 10−8IRG0.571.49
cg07573872191126342SBNO2−0.152.77 × 10−3IRGNSNS
cg078393131917514600BST2 ‖−0.123.48 × 10−3IRGNS2.91
cg215492852142799141MX1 Δ−0.476.59 × 10−13IRG4.054.64
cg194605082244422195PARVB−0.11.64 × 10−3 0.28NS
cg200980152250971140ODF3B−0.219.88 × 10−83IRGNSNS
Differential gene expression values come from GSE32591: kidney glomerulus and tubulointerstitium WHO class 3/4 lupus nephritis versus control samples. NS indicates not significant FDR p-value > 0.2). * CpGs with p < 0.01 and Δβ < −0.085. † Positions are from Build 37. ‡ As defined by Interferome [37]. § SLE patients show decreased expression in both kidney tissues. ‖ Hypomethylation of this gene at the same CpG site has been reported in SLE patients with renal involvement [12]. Hypomethylation of this gene at a different CpG site has been reported in SLE patients with renal involvement [12]. Δ Hypomethylation of this gene at the same CpG site has been reported in SLE patients with and without renal involvement [12]. ◊ Hypomethylation of this gene at a different CpG site has been reported in SLE patients with renal involvement [12].
The DM-DE genes were further analyzed in an additional pathway analysis using the MCODE clustering algorithm and STRING networking scores. Two distinct yet related clusters emerged (Figure 3). As expected, there was an enrichment of genes in the IFN-inducible/pattern recognition receptor pathway. As visually represented by the colors of the nodes and node outlines in Figure 3, all genes in this cluster were upregulated in both active and inactive SLE patients; all of these except PARP9 were overexpressed in both kidney tissues.
The second cluster was comprised of genes involved in the nucleic acid-sensing pathway, a primary antiviral defense in vertebrates as well as a mechanism to respond to intracellular nucleic acids of cellular origin. There were strong links among the genes in these two clusters as this nucleic acid response of the innate immune system results in the production of type 1 interferon (i.e., INF-α and INF-β) and expression of interferon stimulated genes [59]. These hypomethylated genes showed increased expression in both active and inactive SLE patients; the lone exception observed was the reduced expression of PRKCA in active SLE patients. As in the IFN-inducible/pattern recognition receptor pathway, the majority of these nucleic acid-sensing pathway genes were expressed in both kidney tissues. The gene DEAD H-box helicase 58 (DDX58), which encodes for retinoic acid-inducible gene I (RIG-I) [60], was the central node and exhibited the strongest and most numerous links to other genes within the cluster.

3.5. Potential Drug Targets

The DM-DE genes were analyzed for potential gene–drug interactions (Table 4). As evidence of its potential utility, this approach identified methotrexate, a lupus therapy, targeting EPSTI1. Twelve of the DM-DE genes are linked to drugs that are currently in ongoing clinical trials, primarily trials related to cancer (Table 4). The drug target analysis also identified 24 additional FDA-approved drugs linked to genes associated with the nucleic acid-sensing or the interferon-inducible pathways. These drugs could merit careful consideration for future clinical trials in SLE.

4. Discussion

Environmental challenges coupled with genetic susceptibility are often hypothesized to cause the innate and adaptive immune system to become chronically active, causing failure to recognize subsequent autoimmune disease [61]. Aging and environmental exposures such as smoking, chemicals, diet, and viral pathogens predictably trigger methylation or demethylation at CpG sites. Altered methylation of a CpG site changes the accessibility of transcriptional elements to specific regions, which leads to regulation of gene expression. The relationship between DNA methylation and gene expression is complex, including being influenced by specific tissues/cells [62,63,64]. However, in general, DNA methylation in promoter regions is often inversely correlated with gene expression. The above paradigm is consistent with the results of this multi-omic study, which has demonstrated that genes involved in the nucleic acid-sensing and interferon-inducible pathways were observed to be hypomethylated in SLE-affected MZ twins and upregulated in independent SLE cohorts. Despite the clear biological importance of tissue-specific methylation and gene expression, here, the high concordance of hypomethylated genes in whole blood with increased gene expression across a variety of tissues from multiple independent cohorts suggests a high fidelity of the DNA methylation-gene expression relationship at these loci.
Every epigenome-wide study of SLE to date, including this one, has identified hypomethylation of multiple type I IFN-related genes. While there is no doubt that stimulation of the type I IFN pathway is important in SLE, the mechanism by which this stimulation occurs will be unique for each SLE patient. Interferon induction occurs due to activation of one of several types of pattern recognition receptors, which are programmed to respond to double-stranded DNA (dsDNA), double-stranded RNA (dsRNA), or single-stranded RNA (ssRNA). The type of nucleic acid (NA) present will depend on the species and cell type producing the NA. Furthermore, the NA may leak into the cytosome where its recognition is again specific to the receptor activated. In our study, bioinformatic analysis identified the NA-sensing pathway, with DEAD/H-Box helicase 58 (DDX58) as the central node (Figure 3). DDX58 encodes for retinoic acid-inducible gene I (RIG-I), which recognizes ssRNA. In contrast to Toll-like receptors (TLRs), which recognize NAs in the endosome, RIG-I-like receptors (RLRs) interact with mitochondrial antiviral signaling protein (MAVS) in the cytosol [65]. MAVS subsequently phosphorylates interferon regulatory factors 3 (IRF3) to stimulate type 1 IFN expression. The NA-sensing pathway generated by our analysis also included absent in melanoma 2 (AIM2), a cytosolic dsDNA-sensing protein that activates the inflammasome, further emphasizing the plausible role of this pathway in initiating lupus inflammation [66,67].
The cascade of functional consequences resulting from genetic variation and unique environmental exposures will differ for each individual SLE patient. While some SLE patients (10–30%) will present no IFN signature [68], others will overexpress IFN through one of the several mechanisms described above. The DM-SE gene list we prioritized may be a useful tool in grouping SLE patients into DA receptor groups, or “endotypes” as they have been termed by Mustelin et al. [68] Therapies targeting helicases such as RIG-I, MAVS, or AIM2 could prove useful for SLE. One such inhibitor of RIG-I, enhancer of zeste homolog 2 (EZH2), has been shown to play an epigenetic role in SLE and was proposed as a therapeutic target by Tsou et al. [60]. Network analyses and public database queries of our DM-DE genes yielded a list of genes whose products predict gene–drug interactions. The resulting list includes methotrexate, a drug used for the treatment of lupus. The remaining gene–drug interactions we identified merit thorough scrutiny as they could be candidates for future trials.
Three recent studies have observed aberrant methylation of IFN genes in SLE patients with renal involvement [12,19,22]. A summary of the literature (Additional File 2) shows our study’s consistencies with these published findings. While hypomethylation of these genes has been confirmed in CD4+ T cells and peripheral blood, no SLE study to date has examined genome-wide DNA methylation in kidney biopsies. By considering differential gene expression derived from the micro-dissected glomerulus and tubulointerstitium kidneys in an independent cohort of SLE patients, in conjunction with the significance of aberrant methylation in the MZ twin data, this study corroborates many of the loci previously published as being hypomethylated in lupus nephritis patients.
The lack of any differentially methylated genes on the X chromosome is noteworthy given the 9:1 female to male gender bias in SLE. This result is not fully explained by the fact that older female MZ twins show a strong tendency for the same X chromosome to be inactivated [69,70] as the lack of differentially methylated sites on the X chromosome in this study is consistent with previous studies of unrelated individuals [11,15,17,18,19,20,21,23,52]. Jeffries et al., using the Illumina Infinium Human Methylation27 array, did observe differential methylation of CpGs in PCTK1, ARAF, RRAGB, and SNX12 on the X chromosome [11], but no studies utilizing the more recent arrays replicate these findings. In our MZ twin study, CpG sites associated with SNX12 had a minimum p-value = 0.02 (change in β = −0.04), but none of the other three genes had p-values < 0.05. Thus, to date, methylation patterns among genes located on the X chromosome do not appear to explain a substantial portion of the risk of SLE.
Within this study, the genomic locations of hypomethylated CpG sites were highly skewed toward CpG shores (0–2 kb from island) and shelves (2–4 kb from island) instead of islands. Here, only 5 of 59 CpG sites were in a CpG island, despite nearly one third of the CpG sites on the Illumina HumanMethylation450 BeadChip being in a CpG island (Supplemental Figure S2). Our findings are consistent with those of Yeung et al., who demonstrated that most CpG sites hypomethylated in their lupus patients, when compared to controls, were located in CpG shores [21]. These data corroborate the hypothesis that CpG islands tend to have lower methylation rates than less dense CpG regions (e.g., shores and shelves) and that lower density allows for greater methylation autonomy in response to the environment, leading to increases in potential functional significance of the shores and shelves.
There are several limitations of this multi-omics study. One limitation was the modest sample size, as a larger sample would provide the potential to identify additional differentially methylated regions and pathways. However, it is important to recognize the power and value of a discordant MZ twin study design to reduce confounding based on genetic and environmental background. Further, the modest sample size does not negate the positive findings. There were only three discordant MZ twin pairs in the discovery cohort, but we replicated these results in an independent cohort of four MZ twin pairs. Given the number of samples, we were unable to construct and adjust for the full cell composition of the peripheral blood samples as the limited degrees of freedom precluded the robust use of deconvolution methods. Adjusting for latent methylation components in our analysis, while dampening the associations slightly, still identified the same IFN signature. Further, the collective results are supported by larger, independent case–control studies (described in Additional File 2), and we have shown that our methylation results correlate with gene expression in multiple cell types and tissues in independent SLE case–control studies; many were also identified as eQTMs in a Japanese cohort of 100 healthy individuals. We recognize that our cross-sectional study design (i.e., discovery, replication) cannot separate causality from response to disease, but the consistency of differentially methylated regions with the differentially expressed genes from independent gene expression studies is informative and helps identify epigenetically modified genes and pathways that are important in SLE.

5. Conclusions

The intersection of hypomethylated genes from MZ twins and upregulated genes from multiple independent cohorts and cell types were attributed to two distinct but integrated biologic pathways: the nucleic acid-sensing pathway and the IFN-inducing pathway. The source, type, and location of nucleic acids found in an SLE patient determine how and by which receptor the NA is recognized, and ultimately which IRF is stimulated. A multi-omics approach could allow classification of patients into different endotypes and possible treatment groups. Informatically linking the DM-DE genes to drug therapies identified a list of compounds that could be critically evaluated as potential candidates for future trials, either broadly for SLE or for individuals with specific hypomethylation signatures.

Supplementary Materials

The following are available online at https://www.mdpi.com/article/10.3390/genes12121898/s1: Table S1: GEO repository datasets interrogated for differential expression between SLE cases and controls; Table S2: Clinical characteristics of monozygotic twins discordant for SLE; Table S3: Differential expression of hypomethylated genes in peripheral cell types from independent SLE cohorts; Table S4: Differentially methylated probes from three monozygotic twin pairs discordant for SLE; Figure S1: Area under the receiver operating characteristic curve (AUC) calculated at regular intervals between 0 and −0.15 in four cell types; Figure S2: Proportions of significantly associated CpGs (as defined in Table 1) located in islands, shores, shelves, and other content categories; Additional File 1: Differential methylation in MZ twins discordant for SLE; Additional File 2: Replication of CpG sites in Table 1 across published epigenome-wide SLE studies.

Author Contributions

Conceptualization, A.C.G., T.D.H., C.D.L. and P.E.L.; Acquisition of data, A.C.G., T.D.H., J.A.K., C.D.L. and P.E.L.; Analysis and interpretation, R.D.R., H.C.A., P.B., M.D.C., A.C.G., S.E.H., T.D.H., A.C.L., C.D.L., P.E.L., M.C.M., P.S.R. and K.D.Z. Drafting of the manuscript, H.C.A., A.C.G., T.D.H., C.D.L., M.C.M. and P.E.L. All authors have read and agreed to the published version of the manuscript.

Funding

The research reported in this publication was supported by the National Institute of Arthritis and Musculoskeletal and Skin Diseases under award numbers P30AR073750 and N01AR62277, the US Department of Defense under award number W81XWH-20-1-0686, the Wake Forest School of Medicine Center for Public Health Genomics, and research funds from CDL. AC Labonte, SE Heuer, RD Robl, MD Catalina, PE Lipsky, and AC Grammer received financial support from the ALR/LRI (now the Lupus Research Alliance) for drug repositioning work and from the John and Marcia Goldman Foundation for BigData analysis of gene expression from lupus patients compared to normal individuals.

Institutional Review Board Statement

The study was conducted according to the guidelines of the Declaration of Helsinki and approved by the Wake Forest School of Medicine Institutional Review Board (IRB).

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Data Availability Statement

All gene expression datasets are publicly available from the Gene Expression Omnibus (GEO) (Supplemental Table S1). The methylation data from the three monozygotic twins are available upon request from the authors. Samples, including DNA, are available from the Lupus Family Registry and Repository.

Acknowledgments

We would like to thank the Lupus Family Registry and Repository for providing the LFRR twin samples. We also thank the Wake Forest School of Medicine Center for Public Health Genomics for providing computational resources.

Conflicts of Interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.

References

  1. International Consortium for Systemic Lupus Erythematosus Genetics (SLEGEN); Harley, J.B.; Alarcón-Riquelme, M.E.; Criswell, L.A.; Jacob, C.O.; Kimberly, R.P.; Moser, K.L.; Tsao, B.P.; Vyse, T.J.; Langefeld, C.D.; et al. Genome-wide association scan in women with systemic lupus erythematosus identifies susceptibility variants in ITGAM, PXK, KIAA1542 and other loci. Nat. Genet. 2008, 40, 204–210. [Google Scholar] [CrossRef] [PubMed]
  2. Morris, D.L.; Sheng, Y.; Zhang, Y.; Wang, Y.-F.; Zhu, Z.; Tombleson, P.; Chen, L.; Cunninghame Graham, D.S.; Bentham, J.; Roberts, A.L.; et al. Genome-wide association meta-analysis in Chinese and European individuals identifies ten new loci associated with systemic lupus erythematosus. Nat. Genet. 2016, 48, 940–946. [Google Scholar] [CrossRef]
  3. Lessard, C.J.; Sajuthi, S.; Zhao, J.; Kim, K.; Ice, J.A.; Li, H.; Ainsworth, H.; Rasmussen, A.; Kelly, J.A.; Marion, M.; et al. Identification of a Systemic Lupus Erythematosus Risk Locus Spanning ATG16L2, FCHSD2, and P2RY2 in Koreans. Arthritis Rheumatol. 2016, 68, 1197–1209. [Google Scholar] [CrossRef] [Green Version]
  4. Sun, C.; Molineros, J.E.; Looger, L.L.; Zhou, X.-J.; Kim, K.; Okada, Y.; Ma, J.; Qi, Y.-Y.; Kim-Howard, X.; Motghare, P.; et al. High-density genotyping of immune-related loci identifies new SLE risk variants in individuals with Asian ancestry. Nat. Genet. 2016, 48, 323–330. [Google Scholar] [CrossRef] [Green Version]
  5. Alarcón-Riquelme, M.E.; Ziegler, J.T.; Molineros, J.; Howard, T.D.; Moreno-Estrada, A.; Sánchez-Rodríguez, E.; Ainsworth, H.C.; Ortiz-Tello, P.; Comeau, M.E.; Rasmussen, A.; et al. Genome-Wide Association Study in an Amerindian Ancestry Population Reveals Novel Systemic Lupus Erythematosus Risk Loci and the Role of European Admixture. Arthritis Rheumatol. 2016, 68, 932–943. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  6. Langefeld, C.D.; Ainsworth, H.C.; Cunninghame Graham, D.S.; Kelly, J.A.; Comeau, M.E.; Marion, M.C.; Howard, T.D.; Ramos, P.S.; Croker, J.A.; Morris, D.L.; et al. Transancestral mapping and genetic load in systemic lupus erythematosus. Nat. Commun. 2017, 8, 16021. [Google Scholar] [CrossRef]
  7. Deng, Y.; Tsao, B.P. Updates in Lupus Genetics. Curr. Rheumatol. Rep. 2017, 19, 68. [Google Scholar] [CrossRef] [PubMed]
  8. Chen, L.; Morris, D.L.; Vyse, T.J. Genetic advances in systemic lupus erythematosus: An update. Curr. Opin. Rheumatol. 2017, 29, 423–433. [Google Scholar] [CrossRef] [Green Version]
  9. Deapen, D.; Escalante, A.; Weinrib, L.; Horwitz, D.; Bachman, B.; Roy-Burman, P.; Walker, A.; Mack, T.M. A revised estimate of twin concordance in systemic lupus erythematosus. Arthritis Rheum. 1992, 35, 311–318. [Google Scholar]
  10. Hughes, T.; Sawalha, A.H. The role of epigenetic variation in the pathogenesis of systemic lupus erythematosus. Arthritis Res. Ther. 2011, 13, 245. [Google Scholar] [CrossRef] [Green Version]
  11. Jeffries, M.A.; Dozmorov, M.; Tang, Y.; Merrill, J.T.; Wren, J.D.; Sawalha, A.H. Genome-wide DNA methylation patterns in CD4+ T cells from patients with systemic lupus erythematosus. Epigenetics 2011, 6, 593–601. [Google Scholar] [CrossRef] [Green Version]
  12. Coit, P.; Renauer, P.; Jeffries, M.A.; Merrill, J.T.; McCune, W.J.; Maksimowicz-McKinnon, K.; Sawalha, A.H. Renal involvement in lupus is characterized by unique DNA methylation changes in naïve CD4+ T cells. J. Autoimmun. 2015, 61, 29–35. [Google Scholar] [CrossRef] [Green Version]
  13. Renauer, P.; Coit, P.; Jeffries, M.A.; Merrill, J.T.; McCune, W.J.; Maksimowicz-McKinnon, K.; Sawalha, A.H. DNA methylation patterns in naïve CD4+ T cells identify epigenetic susceptibility loci for malar rash and discoid rash in systemic lupus erythematosus. Lupus Sci. Med. 2015, 2, e000101. [Google Scholar] [CrossRef] [Green Version]
  14. Ulff-Møller, C.J.; Asmar, F.; Liu, Y.; Svendsen, A.J.; Busato, F.; Grønbaek, K.; Tost, J.; Jacobsen, S. Twin DNA Methylation Profiling Reveals Flare-Dependent Interferon Signature and B Cell Promoter Hypermethylation in Systemic Lupus Erythematosus. Arthritis Rheumatol. 2018, 70, 878–890. [Google Scholar] [CrossRef] [Green Version]
  15. Coit, P.; Jeffries, M.; Altorok, N.; Dozmorov, M.G.; Koelsch, K.A.; Wren, J.D.; Merrill, J.T.; McCune, W.J.; Sawalha, A.H. Genome-wide DNA methylation study suggests epigenetic accessibility and transcriptional poising of interferon-regulated genes in naïve CD4+ T cells from lupus patients. J. Autoimmun. 2013, 43, 78–84. [Google Scholar] [CrossRef] [Green Version]
  16. Coit, P.; Ognenovski, M.; Gensterblum, E.; Maksimowicz-McKinnon, K.; Wren, J.D.; Sawalha, A.H. Ethnicity-specific epigenetic variation in naïve CD4+ T cells and the susceptibility to autoimmunity. Epigenetics Chromatin 2015, 8, 49. [Google Scholar] [CrossRef] [Green Version]
  17. Absher, D.M.; Li, X.; Waite, L.L.; Gibson, A.; Roberts, K.; Edberg, J.; Chatham, W.W.; Kimberly, R.P. 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. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  18. Coit, P.; Yalavarthi, S.; Ognenovski, M.; Zhao, W.; Hasni, S.; Wren, J.D.; Kaplan, M.J.; Sawalha, A.H. Epigenome profiling reveals significant DNA demethylation of interferon signature genes in lupus neutrophils. J. Autoimmun. 2015, 58, 59–66. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  19. Mok, A.; Solomon, O.; Nayak, R.R.; Coit, P.; Quach, H.L.; Nititham, J.; Sawalha, A.H.; Barcellos, L.F.; Criswell, L.A.; Chung, S.A. Genome-wide profiling identifies associations between lupus nephritis and differential methylation of genes regulating tissue hypoxia and type 1 interferon responses. Lupus Sci. Med. 2016, 3, e000183. [Google Scholar] [CrossRef] [Green Version]
  20. Chung, S.A.; Nititham, J.; Elboudwarej, E.; Quach, H.L.; Taylor, K.E.; Barcellos, L.F.; Criswell, L.A. Genome-Wide Assessment of Differential DNA Methylation Associated with Autoantibody Production in Systemic Lupus Erythematosus. PLoS ONE 2015, 10, e0129813. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  21. Yeung, K.S.; Chung, B.H.-Y.; Choufani, S.; Mok, M.Y.; Wong, W.L.; Mak, C.C.Y.; Yang, W.; Lee, P.P.W.; Wong, W.H.S.; Chen, Y.-A.; et al. Genome-Wide DNA Methylation Analysis of Chinese Patients with Systemic Lupus Erythematosus Identified Hypomethylation in Genes Related to the Type I Interferon Pathway. PLoS ONE 2017, 12, e0169553. [Google Scholar] [CrossRef] [Green Version]
  22. Zhu, H.; Mi, W.; Luo, H.; Chen, T.; Liu, S.; Raman, I.; Zuo, X.; Li, Q.-Z. Whole-genome transcription and DNA methylation analysis of peripheral blood mononuclear cells identified aberrant gene regulation pathways in systemic lupus erythematosus. Arthritis Res. Ther. 2016, 18, 162. [Google Scholar] [CrossRef] [Green Version]
  23. Imgenberg-Kreuz, J.; Carlsson Almlöf, J.; Leonard, D.; Alexsson, A.; Nordmark, G.; Eloranta, M.-L.; Rantapää-Dahlqvist, S.; Bengtsson, A.A.; Jönsen, A.; Padyukov, L.; et al. DNA methylation mapping identifies gene regulatory effects in patients with systemic lupus erythematosus. Ann. Rheum. Dis. 2018, 77, 736–743. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  24. Joseph, S.; George, N.I.; Green-Knox, B.; Treadwell, E.L.; Word, B.; Yim, S.; Lyn-Cook, B. Epigenome-wide association study of peripheral blood mononuclear cells in systemic lupus erythematosus: Identifying DNA methylation signatures associated with interferon-related genes based on ethnicity and SLEDAI. J. Autoimmun. 2019, 96, 147–157. [Google Scholar] [CrossRef]
  25. Breitbach, M.E.; Ramaker, R.C.; Roberts, K.; Kimberly, R.P.; Absher, D. Population-Specific Patterns of Epigenetic Defects in the B Cell Lineage in Patients with Systemic Lupus Erythematosus. Arthritis Rheumatol. 2020, 72, 282–291. [Google Scholar] [CrossRef]
  26. Yeung, K.S.; Lee, T.L.; Mok, M.Y.; Mak, C.C.Y.; Yang, W.; Chong, P.C.Y.; Lee, P.P.W.; Ho, M.H.K.; Choufani, S.; Lau, C.S.; et al. Cell Lineage-specific Genome-wide DNA Methylation Analysis of Patients with Paediatric-onset Systemic Lupus Erythematosus. Epigenetics 2019, 14, 314–351. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  27. Weeding, E.; Sawalha, A.H. Deoxyribonucleic Acid Methylation in Systemic Lupus Erythematosus: Implications for Future Clinical Practice. Front. Immunol. 2018, 9, 875. [Google Scholar] [CrossRef] [PubMed]
  28. Castillo-Fernandez, J.E.; Spector, T.D.; Bell, J.T. Epigenetics of discordant monozygotic twins: Implications for disease. Genome Med. 2014, 6, 60. [Google Scholar] [CrossRef]
  29. Javierre, B.M.; Fernandez, A.F.; Richter, J.; Al-Shahrour, F.; Martin-Subero, J.I.; Rodriguez-Ubreva, J.; Berdasco, M.; Fraga, M.F.; O’Hanlon, T.P.; Rider, L.G.; et al. Changes in the pattern of DNA methylation associate with twin discordance in systemic lupus erythematosus. Genome Res. 2010, 20, 170–179. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  30. Rakyan, V.K.; Beyan, H.; Down, T.A.; Hawa, M.I.; Maslau, S.; Aden, D.; Daunay, A.; Busato, F.; Mein, C.A.; Manfras, B.; et al. Identification of type 1 diabetes-associated DNA methylation variable positions that precede disease diagnosis. PLoS Genet. 2011, 7, e1002300. [Google Scholar] [CrossRef]
  31. Gervin, K.; Vigeland, M.D.; Mattingsdal, M.; Hammerø, M.; Nygård, H.; Olsen, A.O.; Brandt, I.; Harris, J.R.; Undlien, D.E.; Lyle, R. DNA methylation and gene expression changes in monozygotic twins discordant for psoriasis: Identification of epigenetically dysregulated genes. PLoS Genet. 2012, 8, e1002454. [Google Scholar] [CrossRef]
  32. Häsler, R.; Feng, Z.; Bäckdahl, L.; Spehlmann, M.E.; Franke, A.; Teschendorff, A.; Rakyan, V.K.; Down, T.A.; Wilson, G.A.; Feber, A.; et al. A functional methylome map of ulcerative colitis. Genome Res. 2012, 22, 2130–2137. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  33. Rasmussen, A.; Sevier, S.; Kelly, J.A.; Glenn, S.B.; Aberle, T.; Cooney, C.M.; Grether, A.; James, E.; Ning, J.; Tesiram, J.; et al. The lupus family registry and repository. Rheumatology 2011, 50, 47–59. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  34. Hochberg, M.C. Updating the American College of Rheumatology revised criteria for the classification of systemic lupus erythematosus. Arthritis Rheum. 1997, 40, 1725. [Google Scholar] [CrossRef] [PubMed]
  35. Bombardier, C.; Gladman, D.D.; Urowitz, M.B.; Caron, D.; Chang, C.H. Derivation of the SLEDAI. A disease activity index for lupus patients. The Committee on Prognosis Studies in SLE. Arthritis Rheum. 1992, 35, 630–640. [Google Scholar] [CrossRef] [PubMed]
  36. Hochberg, Y. A sharper Bonferroni procedure for multiple tests of significance. Biometrika 1988, 75, 800–802. [Google Scholar] [CrossRef]
  37. Rusinova, I.; Forster, S.; Yu, S.; Kannan, A.; Masse, M.; Cumming, H.; Chapman, R.; Hertzog, P.J. Interferome v2.0: An updated database of annotated interferon-regulated genes. Nucleic Acids Res. 2013, 41, D1040–D1046. [Google Scholar] [CrossRef] [PubMed]
  38. Hachiya, T.; Furukawa, R.; Shiwa, Y.; Ohmomo, H.; Ono, K.; Katsuoka, F.; Nagasaki, M.; Yasuda, J.; Fuse, N.; Kinoshita, K.; et al. Genome-wide identification of inter-individually variable DNA methylation sites improves the efficacy of epigenetic association studies. NPJ Genom Med. 2017, 2, 11. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  39. Wu, J.; Irizarry, R.; MacDonald, J.; Gentry, J. Gcrma: Background adjustment using sequence information. R Package Version 2012, 2200, 3–10. [Google Scholar] [CrossRef]
  40. Ritchie, M.E.; Phipson, B.; Wu, D.; Hu, Y.; Law, C.W.; Shi, W.; Smyth, G.K. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 2015, 43, e47. [Google Scholar] [CrossRef] [PubMed]
  41. Bader, G.D.; Hogue, C.W.V. An automated method for finding molecular complexes in large protein interaction networks. BMC Bioinform. 2003, 4, 2. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  42. Snel, B.; Lehmann, G.; Bork, P.; Huynen, M.A. STRING: A web-server to retrieve and display the repeatedly occurring neighbourhood of a gene. Nucleic Acids Res. 2000, 28, 3442–3444. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  43. Szklarczyk, D.; Franceschini, A.; Wyder, S.; Forslund, K.; Heller, D.; Huerta-Cepas, J.; Simonovic, M.; Roth, A.; Santos, A.; Tsafou, K.P.; et al. STRING v10: Protein-protein interaction networks, integrated over the tree of life. Nucleic Acids Res. 2015, 43, D447–D452. [Google Scholar] [CrossRef] [PubMed]
  44. Griffith, M.; Griffith, O.L.; Coffman, A.C.; Weible, J.V.; McMichael, J.F.; Spies, N.C.; Koval, J.; Das, I.; Callaway, M.B.; Eldred, J.M.; et al. DGIdb: Mining the druggable genome. Nat. Methods 2013, 10, 1209–1210. [Google Scholar] [CrossRef] [PubMed]
  45. Grammer, A.C.; Ryals, M.M.; Heuer, S.E.; Robl, R.D.; Madamanchi, S.; Davis, L.S.; Lauwerys, B.; Catalina, M.D.; Lipsky, P.E. Drug repositioning in SLE: Crowd-sourcing, literature-mining and Big Data analysis. Lupus 2016, 25, 1150–1170. [Google Scholar] [CrossRef]
  46. Mallya, M.; Campbell, R.D.; Aguado, B. Characterization of the five novel Ly-6 superfamily members encoded in the MHC, and detection of cells expressing their potential ligands. Protein Sci. 2006, 15, 2244–2256. [Google Scholar] [CrossRef] [Green Version]
  47. Alaridah, N.; Winqvist, N.; Håkansson, G.; Tenland, E.; Rönnholm, A.; Sturegård, E.; Björkman, P.; Godaly, G. Impaired CXCR1-dependent oxidative defence in active tuberculosis patients. Tuberculosis 2015, 95, 744–750. [Google Scholar] [CrossRef] [Green Version]
  48. Xu, R.; Bao, C.; Huang, H.; Lin, F.; Yuan, Y.; Wang, S.; Jin, L.; Yang, T.; Shi, M.; Zhang, Z.; et al. Low expression of CXCR1/2 on neutrophils predicts poor survival in patients with hepatitis B virus-related acute-on-chronic liver failure. Sci. Rep. 2016, 6, 38714. [Google Scholar] [CrossRef] [Green Version]
  49. Almajhdi, F.N.; Al-Ahdal, M.; Abdo, A.A.; Sanai, F.M.; Al-Anazi, M.; Khalaf, N.; Viswan, N.A.; Al-Ashgar, H.; Al-Kahtani, K.; Al-Humaidan, H.; et al. Single nucleotide polymorphisms in CXCR1 gene and its association with hepatitis B infected patients in Saudi Arabia. Ann. Hepatol. 2013, 12, 220–227. [Google Scholar] [CrossRef]
  50. Swamydas, M.; Gao, J.-L.; Break, T.J.; Johnson, M.D.; Jaeger, M.; Rodriguez, C.A.; Lim, J.K.; Green, N.M.; Collar, A.L.; Fischer, B.G.; et al. CXCR1-mediated neutrophil degranulation and fungal killing promote Candida clearance and host survival. Sci. Transl. Med. 2016, 8, 322ra10. [Google Scholar] [CrossRef] [Green Version]
  51. Gardiner-Garden, M.; Frommer, M. CpG islands in vertebrate genomes. J. Mol. Biol. 1987, 196, 261–282. [Google Scholar] [CrossRef]
  52. Zhao, M.; Zhou, Y.; Zhu, B.; Wan, M.; Jiang, T.; Tan, Q.; Liu, Y.; Jiang, J.; Luo, S.; Tan, Y.; et al. IFI44L promoter methylation as a blood biomarker for systemic lupus erythematosus. Ann. Rheum. Dis. 2016, 75, 1998–2006. [Google Scholar] [CrossRef] [Green Version]
  53. Kiso, K.; Yoshifuji, H.; Oku, T.; Hikida, M.; Kitagori, K.; Hirayama, Y.; Nakajima, T.; Haga, H.; Tsuruyama, T.; Miyagawa-Hayashino, A. Transgelin-2 is upregulated on activated B-cells and expressed in hyperplastic follicles in lupus erythematosus patients. PLoS ONE 2017, 12, e0184738. [Google Scholar] [CrossRef] [Green Version]
  54. Kim, H.-R.; Lee, H.-S.; Lee, K.-S.; Jung, I.D.; Kwon, M.-S.; Kim, C.-H.; Kim, S.-M.; Yoon, M.-H.; Park, Y.-M.; Lee, S.-M.; et al. An Essential Role for TAGLN2 in Phagocytosis of Lipopolysaccharide-activated Macrophages. Sci. Rep. 2017, 7, 8731. [Google Scholar] [CrossRef] [Green Version]
  55. Dragovich, M.A.; Mor, A. The SLAM family receptors: Potential therapeutic targets for inflammatory and autoimmune diseases. Autoimmun. Rev. 2018, 17, 674–682. [Google Scholar] [CrossRef]
  56. Li, F.J.; Won, W.J.; Becker, E.J.; Easlick, J.L.; Tabengwa, E.M.; Li, R.; Shakhmatov, M.; Honjo, K.; Burrows, P.D.; Davis, R.S. Emerging roles for the FCRL family members in lymphocyte biology and disease. Curr. Top. Microbiol. Immunol. 2014, 382, 29–50. [Google Scholar] [CrossRef] [Green Version]
  57. Schreeder, D.M.; Cannon, J.P.; Wu, J.; Li, R.; Shakhmatov, M.A.; Davis, R.S. Cutting edge: FcR-like 6 is an MHC class II receptor. J. Immunol. 2010, 185, 23–27. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  58. Balada, E.; Felip, L.; Ordi-Ros, J.; Vilardell-Tarrés, M. DUSP23 is over-expressed and linked to the expression of DNMTs in CD4+ T cells from systemic lupus erythematosus patients. Clin. Exp. Immunol. 2017, 187, 242–250. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  59. Cavlar, T.; Ablasser, A.; Hornung, V. Induction of type I IFNs by intracellular DNA-sensing pathways. Immunol. Cell Biol. 2012, 90, 474–482. [Google Scholar] [CrossRef]
  60. Suárez-Calvet, X.; Gallardo, E.; Nogales-Gadea, G.; Querol, L.; Navas, M.; Díaz-Manera, J.; Rojas-Garcia, R.; Illa, I. Altered RIG-I/DDX58-mediated innate immunity in dermatomyositis. J. Pathol. 2014, 233, 258–268. [Google Scholar] [CrossRef] [PubMed]
  61. Crow, M.K. Collaboration, genetic associations, and lupus erythematosus. N. Engl. J. Med. 2008, 358, 956–961. [Google Scholar] [CrossRef]
  62. Ambrosi, C.; Manzo, M.; Baubec, T. Dynamics and Context-Dependent Roles of DNA Methylation. J. Mol. Biol. 2017, 429, 1459–1475. [Google Scholar] [CrossRef]
  63. Crowl, J.T.; Gray, E.E.; Pestal, K.; Volkman, H.E.; Stetson, D.B. Intracellular Nucleic Acid Detection in Autoimmunity. Annu. Rev. Immunol. 2017, 35, 313–336. [Google Scholar] [CrossRef]
  64. Schübeler, D. Function and information content of DNA methylation. Nature 2015, 517, 321–326. [Google Scholar] [CrossRef] [PubMed]
  65. Shrivastav, M.; Niewold, T.B. Nucleic Acid sensors and type I interferon production in systemic lupus erythematosus. Front. Immunol. 2013, 4, 319. [Google Scholar] [CrossRef] [Green Version]
  66. Rathinam, V.A.K.; Jiang, Z.; Waggoner, S.N.; Sharma, S.; Cole, L.E.; Waggoner, L.; Vanaja, S.K.; Monks, B.G.; Ganesan, S.; Latz, E.; et al. The AIM2 inflammasome is essential for host defense against cytosolic bacteria and DNA viruses. Nat. Immunol. 2010, 11, 395–402. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  67. Man, S.M.; Karki, R.; Kanneganti, T.-D. AIM2 inflammasome in infection, cancer, and autoimmunity: Role in DNA sensing, inflammation, and innate immunity. Eur. J. Immunol. 2016, 46, 269–280. [Google Scholar] [CrossRef] [Green Version]
  68. Mustelin, T.; Lood, C.; Giltiay, N.V. Sources of Pathogenic Nucleic Acids in Systemic Lupus Erythematosus. Front. Immunol. 2019, 10, 1028. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  69. Christensen, K.; Kristiansen, M.; Hagen-Larsen, H.; Skytthe, A.; Bathum, L.; Jeune, B.; Andersen-Ranberg, K.; Vaupel, J.W.; Orstavik, K.H. X-linked genetic factors regulate hematopoietic stem-cell kinetics in females. Blood 2000, 95, 2449–2451. [Google Scholar] [CrossRef] [PubMed]
  70. Huang, Q.; Parfitt, A.; Grennan, D.M.; Manolios, N. X-chromosome inactivation in monozygotic twins with systemic lupus erythematosus. Autoimmunity 1997, 26, 85–93. [Google Scholar] [CrossRef] [PubMed]
Genes 12 01898 g001 550
Figure 1. Hypomethylated genes showing differential expression in independent SLE cohorts. (A) Specific thresholds for the difference in the β-value (from the discordant twin methylation experiment in whole blood) that maximize the area under the ROC curve predicting increased differential expression in the independent SLE whole blood experiments (GSE39088, GSE49454), monocytes (GSE38351), B-cells (GSE10325, GSE4588), and T cells (GSE10325, GSE51997) are shown as vertical bars. Genes with differential methylation p-values greater than 0.0001 and a mean DNA methylation difference of (Δβ) > |0.025| have been removed from the plots. (B) Heatmap of 43 genes hypomethylated in the discordant twin data (∆β < −0.085) and differentially expressed between controls and active (SLEDAI ≥ 6) or inactive (SLEDAI < 6) lupus patients from two whole blood experiments, monocytes, B cells, and T cells. Hierarchical clustering was performed across rows with Euclidean distance metric and complete linkage. Blue/red gradient represents the log fold change values in lupus patients compared to controls.
Figure 1. Hypomethylated genes showing differential expression in independent SLE cohorts. (A) Specific thresholds for the difference in the β-value (from the discordant twin methylation experiment in whole blood) that maximize the area under the ROC curve predicting increased differential expression in the independent SLE whole blood experiments (GSE39088, GSE49454), monocytes (GSE38351), B-cells (GSE10325, GSE4588), and T cells (GSE10325, GSE51997) are shown as vertical bars. Genes with differential methylation p-values greater than 0.0001 and a mean DNA methylation difference of (Δβ) > |0.025| have been removed from the plots. (B) Heatmap of 43 genes hypomethylated in the discordant twin data (∆β < −0.085) and differentially expressed between controls and active (SLEDAI ≥ 6) or inactive (SLEDAI < 6) lupus patients from two whole blood experiments, monocytes, B cells, and T cells. Hierarchical clustering was performed across rows with Euclidean distance metric and complete linkage. Blue/red gradient represents the log fold change values in lupus patients compared to controls.
Genes 12 01898 g001
Genes 12 01898 g002 550
Figure 2. Pathway analyses of hypomethylated genes showing differential expression in independent SLE cohorts. (A) List and statistical significance of the overlap of the IPA canonical pathways comprised of hypomethylated genes showing differential expression in whole blood of independent SLE patients. (B) IPA canonical IFN signaling of hypomethylated genes showing differential expression (increased expression in SLE cases in red) in whole blood of independent SLE patients. (C) Activation Z-scores of genes predicted as upstream regulators of genes hypomethylated in the discordant twin data (∆β < −0.085) and differentially expressed in whole blood between independent SLE cases and controls. A positive (negative) Z-score indicates that a regulator has significantly more (fewer) activated predictions than inhibited predictions.
Figure 2. Pathway analyses of hypomethylated genes showing differential expression in independent SLE cohorts. (A) List and statistical significance of the overlap of the IPA canonical pathways comprised of hypomethylated genes showing differential expression in whole blood of independent SLE patients. (B) IPA canonical IFN signaling of hypomethylated genes showing differential expression (increased expression in SLE cases in red) in whole blood of independent SLE patients. (C) Activation Z-scores of genes predicted as upstream regulators of genes hypomethylated in the discordant twin data (∆β < −0.085) and differentially expressed in whole blood between independent SLE cases and controls. A positive (negative) Z-score indicates that a regulator has significantly more (fewer) activated predictions than inhibited predictions.
Genes 12 01898 g002
Genes 12 01898 g003 550
Figure 3. MCODE clustering of hypomethylated genes showing differential expression in independent SLE cohorts. A network scoring degree cutoff of 2, node score cutoff of 0.2, k-Core of 2, and a max depth of 100 were applied. Node color indicates log2(FC) direction and node size is inversely scaled with ∆β (larger nodes are more strongly hypomethylated). Edge weight is scaled by STRING protein–protein connectivity score. All upregulated genes present in clusters were also upregulated in inactive SLE WB samples. †, upregulated in kidney glomerulus, WHO class 3/4. ‡, upregulated in kidney tubulointerstitium, WHO class 3/4.
Figure 3. MCODE clustering of hypomethylated genes showing differential expression in independent SLE cohorts. A network scoring degree cutoff of 2, node score cutoff of 0.2, k-Core of 2, and a max depth of 100 were applied. Node color indicates log2(FC) direction and node size is inversely scaled with ∆β (larger nodes are more strongly hypomethylated). Edge weight is scaled by STRING protein–protein connectivity score. All upregulated genes present in clusters were also upregulated in inactive SLE WB samples. †, upregulated in kidney glomerulus, WHO class 3/4. ‡, upregulated in kidney tubulointerstitium, WHO class 3/4.
Genes 12 01898 g003
Table
Table 4. Predicted drugs targeting hypomethylated genes and associated pathways with ∆β < −0.085.
Table 4. Predicted drugs targeting hypomethylated genes and associated pathways with ∆β < −0.085.
CpG *ChrPos(bp) †GeneMean ∆βp-ValueSTITCH [43]IPA ‡DGIdb [44]
cg165260471949893ISG15−0.111.28 × 10−4 Irinotecan F
cg1095965127018020RSAD2−0.133.14 × 10−14Fludarabine F
cg14126601237384708EIF2AK2−0.15.55 × 10−16 Indirubin derivative E804
cg157681382219030752CXCR1−0.117.38 × 10−27Reparixin DReparixin DSCH-527123, Ketoprofen F
cg069813093146260954PLSCR1−0.246.41 × 10−31Wogonin G
cg150653403195632915TNK2−0.164.04 × 10−3Dasatinib−1 FOsimertinib F, VemurafenibFDebromohymenialdisine
cg08099136632811251PSMB8−0.111.43 × 10−4Carfilzomib4 F,Oprozomib D, Bortezomib6 FCarfilzomib4 FCarfilzomib4 F,
cg00052684635694245FKBP5−0.161.65 × 10−3Rapamycin/Sirolimus2 F, Tacrolimus5 F Venlafaxine F, Clomipramine F
cg14864167866751182PDE7A−0.351.21 × 10−9 Ketotifen F, Dyphylline F
cg121104378144098888LY6E−0.23.14 × 10−9 DLYE5953AD
cg061880831091093005IFIT3−0.256.18 × 10−8Imidazoles D
cg0892625311614761IRF7−0.172.01 × 10−9Hesperidin D
cg037531911343566902EPSTI1−0.19.23 × 10−5Methotrexate F T, Vinblastine F, Doxorubicin F, Cisplatin F
cg002469691399159656STK24−0.116.26 × 10−6Staurosporine D
cg233789411764361956PRKCA−0.116.89 × 10−5Staurosporine DAprinocarsenMidostaurin F, Enzastaurin D, Quercetin D G, Aprinocarsen, Ruboxistaurin D, Ingenol Mebutate FW, Bryostatin D, Sotrastaurin Acetate D, Tamoxifen2 F
cg078393131917514600BST2−0.123.48 × 10−3Resveratrol6 D G
cg215492852142799141MX1−0.476.59 × 10−13Mitomycin C F, Colchicine F
cg194605082244422195PARVB−0.11.64 × 10−3Lovastatin3 F Bortezomib6 F
* CpGs with p < 1 × 10−3 and Δβ < −0.085. † Positions are from Build 37. ‡ Qiagen Bioinformatics: ingenuity.com F FDA approved. D Ongoing clinical trial or DiD G GRAS. T Known utility in lupus therapy. FW Ingenol mebutate is FDA-approved in the US but withdrawn in the EU. Numbers in superscript are CoLTS scores and range from −16 to +11.
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Marion, M.C.; Ramos, P.S.; Bachali, P.; Labonte, A.C.; Zimmerman, K.D.; Ainsworth, H.C.; Heuer, S.E.; Robl, R.D.; Catalina, M.D.; Kelly, J.A.; et al. Nucleic Acid-Sensing and Interferon-Inducible Pathways Show Differential Methylation in MZ Twins Discordant for Lupus and Overexpression in Independent Lupus Samples: Implications for Pathogenic Mechanism and Drug Targeting. Genes 2021, 12, 1898. https://doi.org/10.3390/genes12121898

AMA Style

Marion MC, Ramos PS, Bachali P, Labonte AC, Zimmerman KD, Ainsworth HC, Heuer SE, Robl RD, Catalina MD, Kelly JA, et al. Nucleic Acid-Sensing and Interferon-Inducible Pathways Show Differential Methylation in MZ Twins Discordant for Lupus and Overexpression in Independent Lupus Samples: Implications for Pathogenic Mechanism and Drug Targeting. Genes. 2021; 12(12):1898. https://doi.org/10.3390/genes12121898

Chicago/Turabian Style

Marion, Miranda C., Paula S. Ramos, Prathyusha Bachali, Adam C. Labonte, Kip D. Zimmerman, Hannah C. Ainsworth, Sarah E. Heuer, Robert D. Robl, Michelle D. Catalina, Jennifer A. Kelly, and et al. 2021. "Nucleic Acid-Sensing and Interferon-Inducible Pathways Show Differential Methylation in MZ Twins Discordant for Lupus and Overexpression in Independent Lupus Samples: Implications for Pathogenic Mechanism and Drug Targeting" Genes 12, no. 12: 1898. https://doi.org/10.3390/genes12121898

APA Style

Marion, M. C., Ramos, P. S., Bachali, P., Labonte, A. C., Zimmerman, K. D., Ainsworth, H. C., Heuer, S. E., Robl, R. D., Catalina, M. D., Kelly, J. A., Howard, T. D., Lipsky, P. E., Grammer, A. C., & Langefeld, C. D. (2021). Nucleic Acid-Sensing and Interferon-Inducible Pathways Show Differential Methylation in MZ Twins Discordant for Lupus and Overexpression in Independent Lupus Samples: Implications for Pathogenic Mechanism and Drug Targeting. Genes, 12(12), 1898. https://doi.org/10.3390/genes12121898

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop