Systemic sclerosis (SSc or scleroderma) is a rare multisystem, connective tissue disease characterized by cutaneous and visceral fibrosis, immune dysregulation, and vasculopathy. Patients are commonly classified into two main clinical subsets on the basis of the extent of skin thickening: limited or restricted cutaneous SSc (lcSSc) and diffuse or widespread cutaneous SSc (dcSSc). The etiology of SSc remains elusive. The low concordance rate in monozygotic twins and relatively modest genetic burden suggest a substantial role for epigenetic or environmental factors in SSc susceptibility [1, 2]. Environmental factors (e.g., nutrition, behavior, stress) can influence methylation and other epigenetic marks that result in phenotypic change and disease . Thus, epigenetic variation may play an important role in SSc risk.
DNA methylation is a chemical modification of cytosine bases generally associated with transcriptional repression when at regulatory elements such as promoters and enhancers [4, 5]. Nevertheless, the precise relationships between DNA methylation and gene expression are complex and poorly understood [5–8]. The correlation between DNA methylation and gene expression can be positive or negative and is tissue-specific and context-specific, in that the local DNA sequence and genomic features largely account for local patterns of methylation [4, 9–11]. In addition to its potential to affect an individual’s susceptibility to SSc, changes in the methylation of DNA may occur secondarily to SSc and may consequently influence disease progression. There is compelling evidence that DNA methylation plays a role in the pathogenesis of autoimmune diseases, and multiple epigenome-wide association studies revealed the existence of differentially methylated regions associated with, for example, systemic lupus erythematosus (SLE) [12–17], rheumatoid arthritis [18–26], or psoriasis [27–32]. In SSc, differentially methylated genes were reported in an X chromosome analysis of peripheral blood mononuclear cells  and in one genome-wide DNA methylation analysis in dermal fibroblasts . Disease-discordant monozygotic twins offer the ideal study design to investigate the association of DNA methylation with a disease, as it minimizes confounding due to genetic heterogeneity, sex-, age- and early-life environmental effects [35, 36].
To our knowledge, no genome-wide investigation of DNA methylation in whole blood from discordant twins has been reported in SSc. We first conducted epigenomic profiling to investigate the association between DNA methylation variation and SSc. Next, we conducted tissue-specific regulatory annotation and integration with available data from DNA methylation and gene expression profiling studies, with the goal of gaining insights into the potential molecular mechanisms underlying SSc development and/or progression.
Genomic DNA (1 μg) from each individual was treated with sodium bisulfite using the EZ 96-DNA methylation kit (Zymo Research, USA), following the manufacturer’s standard protocol. Genome-wide DNA methylation was assessed in the Genomics Research Core at the University of Pittsburgh using the Illumina Infinium HumanMethylation450 BeadChip (Illumina, 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. Location of individuals on arrays was randomized to minimize potential confounding (e.g., batch effects).
Sample files and expression IDAT files were imported into GenomeStudio Software v.1.9 (Illumina, USA) for primary evaluation of the data. This included initial quality control checks and calculating 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. A negative β-value indicates hypomethylation (i.e., decreased methylation) in the affected SSc twins relative to the unaffected, while a positive β-value indicates hypermethylation (i.e., increased methylation) in the SSc twins relative to the unaffected twins. The data were observed for quality, and a cluster analysis was conducted, using the SNP content, to ensure twins were pairing correctly. Using GenomeStudio, it was noted that the data contained no large batch effects.
After initially inspecting the data with GenomeStudio, the data was opened with the R package ChAMP . When loading the data, probes were dropped if they had a bead count less than 3, if the probed CpG was also an SNP, or if they did not meet a detection p value of 1 × 10−5 (detection p-value is the confidence that a given transcript is expressed above the background defined by negative probes). A total of 447,254 CpGs were used for analysis. The data were then normalized with the same ChAMP package using a BMIQ normalization method. MDS plots based on the 1000 most variable methylation sites were created as a result of the normalization process. These were examined for clustering, and it appeared that samples from individuals of differing ethnicities were clustering together, so some samples were removed to make a more homogenous group that clustered closely together. Singular value decomposition (SVD) was then applied to the matrix to obtain the most significant components of variation. These components were observed in a heat map showing the association between the principal components and the biological factors. To adjust for these batch effects, the ChAMP package employs “ComBat” which uses empirical Bayes methods to correct for technical variation. With the data normalized and batch effects adjusted for, the β-values were outputted to a table for analysis.
A paired t test was computed for each CpG site to test the null hypothesis that the mean difference of β-values for each set of twins is zero (μ = 0). Completing a matched analysis with the paired t test allows us to remove the confounding effects of chronological age, genetic background, ethnicity and admixture, sex, and similarity of the epigenome at birth. All data for monozygotic twins (n = 19) were analyzed first followed by a replication of that analysis with the data for dizygotic twins (n = 8). A meta-analysis of the two separate analyses was then performed using METAL  to get a single p value for each CpG site. False discovery rate (FDR) p values were then calculated for each site, and top results were evaluated. Since no differentially methylated cytosine was identified with FDR-corrected p p values are reported. Only cytosines showing suggestive differential methylation (p −04) in the meta-analysis between the affected and unaffected twin pairs are reported.
Monozygotic twins exhibit increased DNA methylation differences with age . In order to address the effect of age on DNA methylation variation in this study, we cross-referenced our results (all cytosines with suggestive differential methylation (p −04)) against the 490 and the 353 differentially methylated CpG sites associated with age reported by Bell et al.  and Horvath , respectively.
Despite the limited statistical power, an exploratory analysis was computed comparing all twins positive for each of the following clinical features: (1) lung involvement, (2) anticentromere autoantibodies (ACA), and (3) anti-RNA polymerases autoantibodies (anti-RNP), to the twins negative for these criteria. No CpGs met the threshold for suggestive differential methylation (p −04) for any of these clinical features.
We performed genome-wide DNA methylation analysis in whole blood from 27 twin pairs discordant for SSc (Table 1). Monozygotic twins (n = 19) were analyzed first, followed by a replication with the data for dizygotic twins (n = 8). This manuscript reports the results of the meta-analysis of this discovery and replication sets. A total of 155 cytosines showed suggestive differential methylation (p −04) between the affected and unaffected twin pairs, most of which mapped to gene bodies (113, 73%) of 111 unique genes (Additional file 2: Table S1). We note that while a negative β-value (− 1 2: Table S1). The levels of differential methylation between affected and unaffected twin were overall modest, with the largest difference observed in the IFI44L gene (β-value = − 0.12) (Additional file 1: Figure S1). Pathway analysis revealed a significant enrichment of molecules (i.e., gene products) involved in cancer, gastrointestinal disease, and organismal injury and abnormalities (Additional file 2: Table S2).
We also performed DNA methylation analyses in each disease subset. In the meta-analysis of 15 twin pairs discordant for lcSSc, 153 cytosines showed suggestive differential methylation (p −04) between the affected and unaffected twin pairs, most of which mapped to gene bodies (117, 77%) of 115 distinct genes (Additional file 2: Table S3). The differences of methylation levels were modest (β 2: Table S4). A total of 266 cytosines showed suggestive (meta-analysis p −04) differential methylation levels in whole blood from the 9 pairs of twins discordant for dcSSc. The majority of these cytosines mapped to gene bodies (201, 76%) of 196 distinct genes (Additional file 2: Table S5). The largest differences in methylation levels were observed in the hypomethylated IFI44L (β = − 0.17) and DHODH (β = − 0.17) genes. The top molecules were enriched for cancer, gastrointestinal disease, and organismal injury and abnormalities (Additional file 2: Table S6). While there was virtually no overlap of molecules between subsets, with only 1% of common molecules (3/397), there was similar overall enrichment for genes in “cancer” and “gastrointestinal disease.”
Despite the limited statistical power, an exploratory case-case analysis was computed on the following clinical features: (1) lung involvement, (2) anticentromere autoantibodies (ACA), and (3) anti-RNA polymerases autoantibodies (anti-RNP). No CpGs met the threshold for suggestive differential methylation (p −04) for any of these clinical features.
We assessed the overlap between the regions our study unveiled (meta-analysis p −04) and over 40 regions with compelling evidence for genetic association with SSc . The few regions of overlap include the HLA and IRF5 (Additional file 2: Table S7). Since aging can influence DNA methylation variation, we also assessed the overlap between our results and the CpG sites whose methylation levels are strongly correlated with chronological age [42, 44]. Only one age-associated CpG (cg22432269) in the first exon of the CYFIP1 gene showed concomitant evidence of hypomethylation in lcSSc (p = 3.06 × 10−06).
One genome-wide DNA methylation study has been reported in cultured dermal fibroblasts from SSc patients and controls . As expected, given the different tissues profiled, the genes identified in each study are largely different. Of the 30 genes reported by Altorok et al  as common between dcSSc and lcSSc fibroblasts, only CACNA1C was also found among our top results (Additional file 2: Table S2). Additional file 2: Table S8 shows the six CpG sites common to both studies.
To explore the downstream effects of the differentially methylated CpG sites (meta-analysis p −04), the genes corresponding to these CpGs were compared to available data from published global gene expression profiling studies conducted in blood and its cellular subsets from SSc patients and healthy controls. A total of 1907 unique differentially expressed genes were compiled from 8 studies with publicly available results [49–56]. As shown in Table 2, 27 genes with differentially methylated cytosines (in Additional file 2: Tables S1, S3, and S5) have also been reported as differentially expressed in SSc patients. Consistent with the known complex relationships between DNA methylation and gene expression [4–11], for some genes, the relationship between DNA methylation and gene expression was inverse or negative (i.e., increased methylation with decreased gene expression), while for others, it was direct or positive (i.e., increased methylation results in increased gene expression).
Eight noteworthy candidates include IFI44L, where cg03607951 in the transcription start site was hypomethylated in all twins and showed the largest difference in methylation levels in dcSSc. This gene is overexpressed in blood in SSc patients [50, 51, 53] and hypomethylated in multiple blood SLE subsets [15, 45, 46, 48]. The TLE3 gene showed two CpGs in the gene body (cg01666796, cg12349571) with consistent hypermethylation in all affected twins and is also overexpressed in PBMCs in SSc-PAH patients . A CpG (cg22432269) in the first exon of the CYFIP1 gene showed the most significant hypomethylation in lcSSc concomitant with underexpression in PBMCs from lcSSc patients . A CpG site (cg06580770) in the body of TNXB is hypermethylated in blood from SSc and concomitantly underexpressed in SSc patients . Hypomethylated cg15346781 in the transcription start site of the RSAD2 gene is overexpressed in SSc [50, 53] and hypomethylated in T and B cells from SLE patients . Cg24312520 in the gene body of STAT3 was hypermethylated in dcSSc and overexpressed in PBMC from lcSSc and SSc-PAH patients [49, 51]. A first exon cytosine (cg25330422) was reported as hypermethylated in blood cell subsets from SLE patients . The transcription start site TNFRSF1A was both hypermethylated in dcSSc (cg26254667) and overexpressed in SSc and lcSSc [51, 54]. Other gene body CpG sites (cg08418872, cg23752651) have also been reported as hypermethylated in SLE patients . Lastly, cg17925829 in the transcription start site of the TYROBP gene was hypomethylated and the gene overexpressed in SSc [53, 54].
This study used a genome-wide integrative approach to identify differential DNA methylation in whole blood from twin pairs discordant for SSc. In addition to being the largest epigenomic study conducted in SSc to date, the unique study design minimizes confounding due to genetic heterogeneity and age- and early-life environmental effects by using disease-discordant twins [35, 36]. As expected, given the sample size, we did not detect genome-wide significant differences in mean DNA methylation associated with SSc, which is largely consistent with other complex disease epigenomic twin studies [12, 57, 58]. The results revealed distinct DNA methylation patterns in SSc and its clinical disease subsets. The negligible overlap of molecules shared between the lcSSc and dcSSc subsets supports distinct epigenetic architectures in each disease subset. Despite clearly distinct blood methylation profiles, an enrichment of genes in “cancer” and “gastrointestinal disease” was observed in both dcSSc and lcSSc, although driven by different molecules. These results are consistent with the previously reported minimal common differentially methylated cytosines between lcSSc and dcSSc subsets in skin fibroblasts . In addition, our analyses revealed negligible overlap between the methylation patterns in whole blood and those previously reported in skin fibroblasts . Thus, although SSc is commonly considered a single disease, these results confirm others suggesting that SSc is a family of diseases with distinctly different subtypes.
The precise relationships between DNA methylation and gene expression are complex and poorly understood [4–11]. While DNA methylation at regulatory elements shows a negative correlation with transcription, the opposite has been observed at intragenic regions , illustrating that complex regulatory mechanisms that are dependent on the tissue and genomic architecture underlie the correlation between DNA methylation and gene expression. It is also possible that the low correlation between DNA methylation and gene expression levels may reflect high fluctuation of RNA levels, which can change from 1 h to the next . In order to provide insights into the potential functional consequences of the methylation patterns observed, we compared our results to those of global gene expression profiling assays conducted in blood and its cellular subsets from SSc patients and healthy controls. This study unveiled several novel genes epigenetically dysregulated with reported changes in gene expression in blood from SSc patients. Most of these genes are involved in immune processes.
IFI44L, an interferon gene involved in defense response to viruses, is overexpressed in SSc blood tissues [50, 51, 53]. The CpG site unveiled in our study shows consistent hypomethylation in multiple blood cell subsets from SLE [15, 45–48, 60] and Sjögren’s syndrome patients [61, 62]. Since SSc and SLE are often considered as sister diseases, reported DNA methylation similarities are not unexpected . The consistent hypomethylation of IFI44L in blood from patients with several autoimmune diseases, together with its overexpression, corroborates the validity of our finding and suggests that differential methylation of IFI44L may serve as shared biomarker across these diseases.
Both RSAD2 and TYROBP showed hypomethylation and overexpression in SSc blood [50, 53, 54]. Both play roles in immune response, including type I IFN signaling pathway (RSAD2) and innate immunity (TYROBP). RSAD2 is consistently hypomethylated in blood cells [15, 47]. Demethylation of the TYROBP gene is associated with a subset of T cells that accumulates and is associated with aging . An age-associated CpG  in CYFIP1, a regulator of translation and cytoskeletal dynamics, showed hypomethylation with underexpression in SSc blood . It is interesting to note the variation in methylation levels at sites associated with aging, as premature activation of aging-associated molecular mechanisms is emerging as an important contributor to the autoimmune, vascular, and fibrotic pathogenesis of SSc . Our findings, in conjunction with these reports, further lend support for the role of the innate immune response in the pathogenesis and/or progression of diseases such as SSc and a parallel between SSc and premature aging.
Differential methylation of several genes has been reported as associated with cancer [66–69]. These include TNFRSF1A, which plays a role in cell survival, apoptosis, and inflammation and was both hypermethylated and overexpressed in SSc blood [51, 54]. TLE3 was also hypermethylated and overexpressed in SSc blood . This gene product functions in the Notch signaling pathway to regulate the determination of cell fate during development. STAT3, a transcription activator with roles in many cellular processes such as cell growth, apoptosis, and response to cytokines and growth factors, showed hypermethylation and overexpression in SSc blood [49, 51]. TNXB, which was hypomethylated in skin fibroblasts from dcSSc , was hypermethylated in our study and concomitantly underexpressed in blood from SSc patients . This gene localizes to the MHC class III region and encodes a member of the tenascin family of extracellular matrix glycoproteins. It is involved in actin cytoskeleton organization, cell adhesion, and collagen fibril organization.
To aid in result interpretation, regulatory annotation of the top differentially methylated cytosines was conducted to predict disease-relevant cell types. Differential DNA methylations in regulatory regions such as DHS and histone marks have been associated with functional consequences [4, 70]. We observed an enrichment of regulatory regions in the dcSSc subset that pointed to blood myeloid cells as the most highly enriched cell types, indicating a tendency for cell-composition-corrected dcSSc-associated DNA methylation changes to co-locate with myeloid cell DHSs and H3K4me1 marks (representative of enhancers). This contrasts with an enrichment in DHSs specific to T cells that was reported using cytosines differentially methylated in CD4+ T cell studies of SLE and Sjögren’s syndrome . This enrichment of methylated cytosines in regulatory regions in myeloid cells might underlie a dysregulation of these cells in dcSSc. Indeed, both monocytes and macrophages (cell types with the strongest enrichment) play a critical role in fibrosis . The number of circulating monocytes is increased in SSc  and correlates with disease progression and severity [73, 74]. The changes in methylation detected in dcSSc are thus impacting the function of regulatory elements in cell types with critical functions in fibrosis. Since these inflammatory cells are dysregulated in SSc, and DNA methylation changes can affect regulatory mechanisms, our findings suggest that DNA methylation might be a potential avenue to reverse their altered phenotype.
This study has a number of limitations. Despite the value of the twin-pair study design for epigenomic studies, our unique samples of middle-aged, European ancestry, largely female twin pairs are not representative of the general population. Thus, our results might not be generalizable to all patients. Further replication studies are warranted for the validation, justification, and generalization of our results. Another limitation is the lack of available RNA from the same samples to assess the functional effects of the variation in DNA methylation. In an attempt to circumvent this limitation, we performed in silico integration with reported differentially expressed genes for functional validation of our results. Documenting that differentially methylated sites in our twin data also correspond to differences in gene expression in independent SSc samples forms corroborating evidence across genomic processes and cohorts. A further limitation is the lack of tissue specificity. We explored this issue by performing regulatory annotation of our results, but future work is needed to dissect the tissue specificity of epigenetic modifications in SSc. We cannot exclude the possibility that the differences between the disease subsets and enrichment of myeloid-related cells in dcSSc are driven by confounding cell-composition effects instead of true cell type-specific effects. However, whole blood lymphocytes are proportionally more abundant than monocytes, suggesting that the strong bias towards monocytes and macrophages is a cell type-specific effect. Multiple differentially methylated cytosines in our study were also found to be differentially methylated in a single blood cell type in SLE, suggesting that the associations we detected are not likely to be due to confounding by blood cell heterogeneity. These include, among others, loci in the IFI44L, RSAD2, IRF5, and RPTOR genes . In spite of these limitations, these findings identify novel genomic regions in SSc in a unique cohort of discordant twins and highlight candidate genes for further research.
We identified multiple DNA methylation loci associated with SSc, including sites with concomitant evidence of altered methylation in blood cells of lupus patients and genes with concomitant evidence of differential expression in blood cells from SSc patients. Although this cross-sectional study cannot separate causality from response to disease, it identifies epigenetically modified genes and pathways that are important in SSc.
Our study hence provides support for using blood cells as a useful accessible tissue for epigenetic biomarker discovery. Our results show that DNA methylation sites in dcSSc patients are enriched for regulatory regions in cell types with key roles in fibrosis, implicating DNA methylation as a modulator of cell functionality. Coupled with the observation that dcSSc and lcSSc are epigenetically distinct disease subtypes, this suggests that the cellular dysfunction observed in dcSSc is, at least partially, due to an epigenetic dysregulation of myeloid cell types. Further, this suggests the possibility of using epigenetic regulation of cell functionality to prevent dysfunction or restore their balance in SSc. Regardless of causality, blood-based biomarkers have the potential to improve risk prediction and help guide treatment decisions. Our findings provide a foundation for further research to determine if the differentially methylated functional loci represent attractive targets for the treatment or prevention of autoimmune- and/or fibrotic-related diseases.
Ramos PS, Silver RM, Feghali-Bostwick CA. Genetics of systemic sclerosis: recent advances. Curr Opin Rheumatol. 2015;27:521–9. View ArticleGoogle Scholar
Feghali-Bostwick C, Medsger TA Jr, Wright TM. Analysis of systemic sclerosis in twins reveals low concordance for disease and high concordance for the presence of antinuclear antibodies. Arthritis Rheum. 2003;48:1956–63. View ArticleGoogle Scholar
Faulk C, Dolinoy DC. Timing is everything: the when and how of environmentally induced changes in the epigenome of animals. Epigenetics. 2011;6:791–7. View ArticleGoogle Scholar
Luo C, Hajkova P, Ecker JR. Dynamic DNA methylation: in the right place at the right time. Science. 2018;361:1336–40. View ArticleGoogle Scholar
Pai AA, Pritchard JK, Gilad Y. The genetic and mechanistic basis for variation in gene regulation. PLoS Genet. 2015;11:e1004857. View ArticleGoogle Scholar
Gutierrez-Arcelus M, Lappalainen T, Montgomery SB, Buil A, Ongen H, Yurovsky A, Bryois J, Giger T, Romano L, Planchon A, et al. Passive and active DNA methylation and the interplay with genetic variation in gene regulation. eLife. 2013;2:e00523. View ArticleGoogle Scholar
Pacis A, Tailleux L, Morin AM, Lambourne J, MacIsaac JL, Yotova V, Dumaine A, Danckaert A, Luca F, Grenier JC, et al. Bacterial infection remodels the DNA methylation landscape of human dendritic cells. Genome Res. 2015;25:1801–11. View ArticleGoogle Scholar
Gutierrez-Arcelus M, Ongen H, Lappalainen T, Montgomery SB, Buil A, Yurovsky A, Bryois J, Padioleau I, Romano L, Planchon A, et al. Tissue-specific effects of genetic and epigenetic variation on gene regulation and splicing. PLoS Genet. 2015;11:e1004958. View ArticleGoogle Scholar
Teschendorff AE, Relton CL. Statistical and integrative system-level analysis of DNA methylation data. Nat Rev Genet. 2018;19:129–47. View ArticleGoogle Scholar
Ambrosi C, Manzo M, Baubec T. Dynamics and context-dependent roles of DNA methylation. J Mol Biol. 2017;429:1459–75. View ArticleGoogle Scholar
Javierre BM, Fernandez AF, Richter J, Al-Shahrour F, Martin-Subero JI, Rodriguez-Ubreva J, Berdasco M, Fraga MF, O'Hanlon TP, Rider LG, et al. Changes in the pattern of DNA methylation associate with twin discordance in systemic lupus erythematosus. Genome Res. 2010;20:170–9. View ArticleGoogle Scholar
Lin SY, Hsieh SC, Lin YC, Lee CN, Tsai MH, Lai LC, Chuang EY, Chen PC, Hung CC, Chen LY, et al. 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. View ArticleGoogle Scholar
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. View ArticleGoogle Scholar
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. View ArticleGoogle Scholar
Zhao M, Liu S, Luo S, Wu H, Tang M, Cheng W, Zhang Q, Zhang P, Yu X, Xia Y, et al. DNA methylation and mRNA and microRNA expression of SLE CD4+ T cells correlate with disease phenotype. J Autoimmun. 2014;54:127–36. View ArticleGoogle Scholar
Coit P, Jeffries M, Altorok N, Dozmorov MG, Koelsch KA, Wren JD, Merrill JT, McCune WJ, Sawalha AH. Genome-wide DNA methylation study suggests epigenetic accessibility and transcriptional poising of interferon-regulated genes in naive CD4+ T cells from lupus patients. J Autoimmun. 2013,43:78–84.Google Scholar
Webster AP, Plant D, Ecker S, Zufferey F, Bell JT, Feber A, Paul DS, Beck S, Barton A, Williams FMK, et al. Increased DNA methylation variability in rheumatoid arthritis-discordant monozygotic twins. Genome Med. 2018;10:64. View ArticleGoogle Scholar
van Steenbergen HW, Luijk R, Shoemaker R, Heijmans BT, Huizinga TW, van der Helm-van Mil AH. Differential methylation within the major histocompatibility complex region in rheumatoid arthritis: a replication study. Rheumatology (Oxford). 2014;53:2317–8. View ArticleGoogle Scholar
Nakano K, Whitaker JW, Boyle DL, Wang W, Firestein GS. DNA methylome signature in rheumatoid arthritis. Ann Rheum Dis. 2013;72:110–7. View ArticleGoogle Scholar
Liu Y, Aryee MJ, Padyukov L, Fallin MD, Hesselberg E, Runarsson A, Reinius L, Acevedo N, Taub M, Ronninger M, et al. Epigenome-wide association data implicate DNA methylation as an intermediary of genetic risk in rheumatoid arthritis. Nat Biotechnol. 2013;31:142–7. View ArticleGoogle Scholar
Glossop JR, Emes RD, Nixon NB, Haworth KE, Packham JC, Dawes PT, Fryer AA, Mattey DL, Farrell WE. Genome-wide DNA methylation profiling in rheumatoid arthritis identifies disease-associated methylation changes that are distinct to individual T- and B-lymphocyte populations. Epigenetics. 2014;9:1228–37. View ArticleGoogle Scholar
de la Rica L, Urquiza JM, Gomez-Cabrero D, Islam AB, Lopez-Bigas N, Tegner J, Toes RE, Ballestar E. Identification of novel markers in rheumatoid arthritis through integrated analysis of DNA methylation and microRNA expression. J Autoimmun. 2013;41:6–16. View ArticleGoogle Scholar
Julia A, Absher D, Lopez-Lasanta M, Palau N, Pluma A, Waite Jones L, Glossop JR, Farrell WE, Myers RM, Marsal S. Epigenome-wide association study of rheumatoid arthritis identifies differentially methylated loci in B cells. Hum Mol Genet. 2017;26:2803–11. View ArticleGoogle Scholar
Zhu H, Wu LF, Mo XB, Lu X, Tang H, Zhu XW, Xia W, Guo YF, Wang MJ, Zeng KQ et al. Rheumatoid arthritis-associated DNA methylation sites in peripheral blood mononuclear cells. Ann Rheum Dis. 2019,78:36–42.Google Scholar
Ai R, Hammaker D, Boyle DL, Morgan R, Walsh AM, Fan S, Firestein GS, Wang W. Joint-specific DNA methylation and transcriptome signatures in rheumatoid arthritis identify distinct pathogenic processes. Nat Commun. 2016;7:11849. View ArticleGoogle Scholar
Gervin K, Vigeland MD, Mattingsdal M, Hammero M, Nygard H, Olsen AO, Brandt I, Harris JR, Undlien DE, Lyle R. DNA methylation and gene expression changes in monozygotic twins discordant for psoriasis: identification of epigenetically dysregulated genes. PLoS Genet. 2012;8:e1002454. View ArticleGoogle Scholar
Park GT, Han J, Park SG, Kim S, Kim TY. DNA methylation analysis of CD4+ T cells in patients with psoriasis. Arch Dermatol Res. 2014;306:259–68. View ArticleGoogle Scholar
Zhang P, Zhao M, Liang G, Yin G, Huang D, Su F, Zhai H, Wang L, Su Y, Lu Q. Whole-genome DNA methylation in skin lesions from patients with psoriasis vulgaris. J Autoimmun. 2013;41:17–24. View ArticleGoogle Scholar
Chandra A, Senapati S, Roy S, Chatterjee G, Chatterjee R. Epigenome-wide DNA methylation regulates cardinal pathological features of psoriasis. Clin Epigenetics. 2018;10:108. View ArticleGoogle Scholar
Zhou F, Shen C, Xu J, Gao J, Zheng X, Ko R, Dou J, Cheng Y, Zhu C, Xu S, et al. Epigenome-wide association data implicates DNA methylation-mediated genetic risk in psoriasis. Clin Epigenetics. 2016;8:131. View ArticleGoogle Scholar
Roberson ED, Liu Y, Ryan C, Joyce CE, Duan S, Cao L, Martin A, Liao W, Menter A, Bowcock AM. A subset of methylated CpG sites differentiate psoriatic from normal skin. J Invest Dermatol. 2012;132:583–92. View ArticleGoogle Scholar
Selmi C, Feghali-Bostwick CA, Lleo A, Lombardi SA, De Santis M, Cavaciocchi F, Zammataro L, Mitchell MM, Lasalle JM, Medsger T Jr, et al. X chromosome gene methylation in peripheral lymphocytes from monozygotic twins discordant for scleroderma. Clin Exp Immunol. 2012;169:253–62. View ArticleGoogle Scholar
Altorok N, Tsou PS, Coit P, Khanna D, Sawalha AH. Genome-wide DNA methylation analysis in dermal fibroblasts from patients with diffuse and limited systemic sclerosis reveals common and subset-specific DNA methylation aberrancies. Ann Rheum Dis. 2015,74:1612–20.Google Scholar
Birney E, Smith GD, Greally JM. Epigenome-wide association studies and the interpretation of disease -omics. PLoS Genet. 2016;12:e1006105. View ArticleGoogle Scholar
Castillo-Fernandez JE, Spector TD, Bell JT. Epigenetics of discordant monozygotic twins: implications for disease. Genome Med. 2014;6:60. View ArticleGoogle Scholar
LeRoy EC, Medsger TA Jr. Criteria for the classification of early systemic sclerosis. J Rheumatol. 2001;28:1573–6. PubMedGoogle Scholar
Willer CJ, Li Y, Abecasis GR. METAL: fast and efficient meta-analysis of genomewide association scans. Bioinformatics. 2010;26:2190–1. View ArticleGoogle Scholar
Fraga MF, Ballestar E, Paz MF, Ropero S, Setien F, Ballestar ML, Heine-Suner D, Cigudosa JC, Urioste M, Benitez J, et al. Epigenetic differences arise during the lifetime of monozygotic twins. Proc Natl Acad Sci U S A. 2005;102:10604–9. View ArticleGoogle Scholar
Bell JT, Tsai PC, Yang TP, Pidsley R, Nisbet J, Glass D, Mangino M, Zhai G, Zhang F, Valdes A, et al. Epigenome-wide scans identify differentially methylated regions for age and age-related phenotypes in a healthy ageing population. PLoS Genet. 2012;8:e1002629. View ArticleGoogle Scholar
Breeze CE, Paul DS, van Dongen J, Butcher LM, Ambrose JC, Barrett JE, Lowe R, Rakyan VK, Iotchkova V, Frontini M, et al. eFORGE: a tool for identifying cell type-specific signal in epigenomic data. Cell Rep. 2016;17:2137–50. View ArticleGoogle Scholar
Weidner CI, Lin Q, Koch CM, Eisele L, Beier F, Ziegler P, Bauerschlag DO, Jockel KH, Erbel R, Muhleisen TW, et al. Aging of blood can be tracked by DNA methylation changes at just three CpG sites. Genome Biol. 2014;15:R24. View ArticleGoogle Scholar
Coit P, Yalavarthi S, Ognenovski M, Zhao W, Hasni S, Wren JD, Kaplan MJ, Sawalha AH. Epigenome profiling reveals significant DNA demethylation of interferon signature genes in lupus neutrophils. J Autoimmun. 2015,58:59–66.Google Scholar
Coit P, Yalavarthi S, Ognenovski M, Zhao W, Hasni S, Wren JD, Kaplan MJ, Sawalha AH. Epigenome profiling reveals significant DNA demethylation of interferon signature genes in lupus neutrophils. J Autoimmun. 2015;58:59–66. View ArticleGoogle Scholar
Zhu H, Mi W, Luo H, Chen T, Liu S, Raman I, Zuo X, Li QZ. 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. View ArticleGoogle Scholar
Yeung KS, Chung BH, Choufani S, Mok MY, Wong WL, Mak CC, Yang W, Lee PP, Wong WH, Chen YA, 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. View ArticleGoogle Scholar
Grigoryev DN, Mathai SC, Fisher MR, Girgis RE, Zaiman AL, Housten-Harris T, Cheadle C, Gao L, Hummers LK, Champion HC, et al. Identification of candidate genes in scleroderma-related pulmonary arterial hypertension. Transl Res. 2008;151:197–207. View ArticleGoogle Scholar
Assassi S, Mayes MD, Arnett FC, Gourh P, Agarwal SK, McNearney TA, Chaussabel D, Oommen N, Fischbach M, Shah KR, et al. Systemic sclerosis and lupus: points in an interferon-mediated continuum. Arthritis Rheum. 2010;62:589–98. View ArticleGoogle Scholar
Christmann RB, Hayes E, Pendergrass S, Padilla C, Farina G, Affandi AJ, Whitfield ML, Farber HW, Lafyatis R. Interferon and alternative activation of monocyte/macrophages in systemic sclerosis-associated pulmonary arterial hypertension. Arthritis Rheum. 2011;63:1718–28. View ArticleGoogle Scholar
Hudson M, Bernatsky S, Colmegna I, Lora M, Pastinen T, Klein Oros K, Greenwood CMT. Novel insights into systemic autoimmune rheumatic diseases using shared molecular signatures and an integrative analysis. Epigenetics. 2017;12:433–40. View ArticleGoogle Scholar
Bos CL, van Baarsen LG, Timmer TC, Overbeek MJ, Basoski NM, Rustenburg F, Baggen JM, Thiesen HJ, Dijkmans BA, van der Pouw Kraan TC, et al. Molecular subtypes of systemic sclerosis in association with anti-centromere antibodies and digital ulcers. Genes Immun. 2009;10:210–8. View ArticleGoogle Scholar
Tan FK, Zhou X, Mayes MD, Gourh P, Guo X, Marcum C, Jin L, Arnett FC Jr. Signatures of differentially regulated interferon gene expression and vasculotrophism in the peripheral blood cells of systemic sclerosis patients. Rheumatology (Oxford). 2006;45:694–702. View ArticleGoogle Scholar
York MR, Nagai T, Mangini AJ, Lemaire R, van Seventer JM, Lafyatis R. A macrophage marker, Siglec-1, is increased on circulating monocytes in patients with systemic sclerosis and induced by type I interferons and toll-like receptor agonists. Arthritis Rheum. 2007;56:1010–20. View ArticleGoogle Scholar
Pendergrass SA, Hayes E, Farina G, Lemaire R, Farber HW, Whitfield ML, Lafyatis R. Limited systemic sclerosis patients with pulmonary arterial hypertension show biomarkers of inflammation and vascular injury. PLoS One. 2010;5:e12106. View ArticleGoogle Scholar
Paul DS, Teschendorff AE, Dang MA, Lowe R, Hawa MI, Ecker S, Beyan H, Cunningham S, Fouts AR, Ramelius A, et al. Increased DNA methylation variability in type 1 diabetes across three immune effector cell types. Nat Commun. 2016;7:13555. View ArticleGoogle Scholar
Rakyan VK, Beyan H, Down TA, Hawa MI, Maslau S, Aden D, Daunay A, Busato F, Mein CA, Manfras B, et al. Identification of type 1 diabetes-associated DNA methylation variable positions that precede disease diagnosis. PLoS Genet. 2011;7:e1002300. View ArticleGoogle Scholar
Horvath S, Raj K. DNA methylation-based biomarkers and the epigenetic clock theory of ageing. Nat Rev Genet. 2018;19:371–84. View ArticleGoogle Scholar
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. View ArticleGoogle Scholar
Imgenberg-Kreuz J, Sandling JK, Almlof JC, Nordlund J, Signer L, Norheim KB, Omdal R, Ronnblom L, Eloranta ML, Syvanen AC, et al. Genome-wide DNA methylation analysis in multiple tissues in primary Sjogren’s syndrome reveals regulatory effects at interferon-induced genes. Ann Rheum Dis. 2016;75:2029–36. View ArticleGoogle Scholar
Altorok N, Coit P, Hughes T, Koelsch KA, Stone DU, Rasmussen A, Radfar L, Scofield RH, Sivils KL, Farris AD, et al. Genome-wide DNA methylation patterns in naive CD4+ T cells from patients with primary Sjogren’s syndrome. Arthritis Rheumatol. 2014;66:731–9. View ArticleGoogle Scholar
Matatiele P, Tikly M, Tarr G, Gulumian M. DNA methylation similarities in genes of black South Africans with systemic lupus erythematosus and systemic sclerosis. J Biomed Sci. 2015;22:34. View ArticleGoogle Scholar
Suarez-Alvarez B, Rodriguez RM, Schlangen K, Raneros AB, Marquez-Kisinousky L, Fernandez AF, Diaz-Corte C, Aransay AM, Lopez-Larrea C. Phenotypic characteristics of aged CD4+ CD28null T lymphocytes are determined by changes in the whole-genome DNA methylation pattern. Aging Cell. 2017;16:293–303. View ArticleGoogle Scholar
Wyman AE, Atamas SP. Sirtuins and accelerated aging in scleroderma. Curr Rheumatol Rep. 2018;20:16. View ArticleGoogle Scholar
Saito Y, Nagae G, Motoi N, Miyauchi E, Ninomiya H, Uehara H, Mun M, Okumura S, Ohyanagi F, Nishio M, et al. Prognostic significance of CpG island methylator phenotype in surgically resected small cell lung carcinoma. Cancer Sci. 2016;107:320–5. View ArticleGoogle Scholar
Farkas SA, Vymetalkova V, Vodickova L, Vodicka P, Nilsson TK. DNA methylation changes in genes frequently mutated in sporadic colorectal cancer and in the DNA repair and Wnt/beta-catenin signaling pathway genes. Epigenomics. 2014;6:179–91. View ArticleGoogle Scholar
Valiulyte I, Steponaitis G, Skiriute D, Tamasauskas A, Vaitkiene P. Signal transducer and activator of transcription 3 (STAT3) promoter methylation and expression in pituitary adenoma. BMC Med Genet. 2017;18:72. View ArticleGoogle Scholar
Men C, Chai H, Song X, Li Y, Du H, Ren Q. Identification of DNA methylation associated gene signatures in endometrial cancer via integrated analysis of DNA methylation and gene expression systematically. J Gynecol Oncol. 2017;28:e83. View ArticleGoogle Scholar
Leenen FA, Muller CP, Turner JD. DNA methylation: conducting the orchestra from exposure to phenotype? Clin Epigenetics. 2016;8:92. View ArticleGoogle Scholar
Wynn TA, Vannella KM. Macrophages in tissue repair, regeneration, and fibrosis. Immunity. 2016;44:450–62. View ArticleGoogle Scholar
Brunasso AM, Massone C. Update on the pathogenesis of Scleroderma: focus on circulating progenitor cells. F1000Research. 2016;5.Google Scholar
Lescoat A, Lecureur V, Roussel M, Sunnaram BL, Ballerie A, Coiffier G, Jouneau S, Fardel O, Fest T, Jego P. CD16-positive circulating monocytes and fibrotic manifestations of systemic sclerosis. Clin Rheumatol. 2017,36:1649–54. View ArticleGoogle Scholar
© The Author(s). 2019
Also in Industry News
NEW YORK (GenomeWeb) – Genetic factors that contribute to a highly heritable developmental condition called Hirschsprung's disease include a complex suite of risk variants, ranging from common polymorphisms in non-coding elements to rarer coding variants and copy number variants (CNVs), according to new research from investigators at Johns Hopkins University, the University of Washington, the Broad Institute, and New York University.
"In our study, we found that the risk of the complex phenotype...
In the publication of this article , there is an error in one of the contributing author names.
The error: ‘Jalal Abdolali Zade’
Should instead read: ‘Jalal Abdolalizadeh’
Open AccessThis article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, prov...
Date: Tuesday, 11 June, 2019
Time: 09:30 – 13:00
Place: Fèlix Serratosa, Parc Cientific de Barcelona (PCB)
This short workshop will describe many aspects of heterologous expression in E.coli, from choice of construct design through to methods to improve the levels of soluble expression and options for co-expression. In addition, it will also address expression in eukaryotic hosts, including secreted proteins and ECDs, and options for co-expression.
The workshop will finish with a discussion an...