X chromosome gene methylation in peripheral lymphocytes from monozygotic twins discordant for scleroderma

Abstract

Scleroderma (SSc) is a rare connective tissue disease characterized by fibrosis, microvasculopathy and autoimmune features. The role of genetics is limited in SSc, as suggested by similar concordance rates in monozygotic and dizygotic twin pairs, while environmental factors may act through epigenetic changes, as demonstrated for specific genes. Further, sex chromosome changes have been reported in SSc and may explain the female preponderance. In the present study we compared the methylation profile of all X chromosome genes in peripheral blood mononuclear cells from monozygotic twins discordant (n = 7) and concordant (n = 1) for SSc. Methylated DNA immunoprecipitations from each discordant twin pair were hybridized to a custom-designed array included 998 sites encompassing promoters of all X chromosome genes and randomly chosen autosomal genes. Biostatistical tools identified sites with an elevated probability to be consistently hypermethylated (n = 18) or hypomethylated (n = 25) in affected twins. Identified genes include transcription factors (ARX, HSFX1, ZBED1, ZNF41) and surface antigens (IL1RAPL2, PGRMC1), and pathway analysis suggests their involvement in cell proliferation (PGK1, SMS, UTP14A, SSR4), apoptosis (MTM1), inflammation (ARAF) and oxidative stress (ENOX2). In conclusion, we propose that X chromosome genes with different methylation profiles in monozygotic twin pairs may constitute candidates for SSc susceptibility.

Introduction

Scleroderma or systemic sclerosis (SSc) is a multi-system connective tissue disease characterized by widespread skin and visceral fibrosis, microangiopathy and immunological features such as serum autoantibodies and female predominance [1]. Similar to other autoimmune diseases, genetic and environmental factors appear to contribute to disease susceptibility and the comparison of disease concordance rates in monozygotic (MZ) versus dizygotic (DZ) twins is a powerful strategy to estimate their relative weight [2]. Concordance rates for autoimmune diseases in MZ twins are largely below 50% with few exceptions, but remain higher compared to DZ twins or siblings [2]. In the case of SSc, similar concordance rates have been observed in MZ (4·2%) and DZ twins (5·6%) in a cross-sectional study [3], while a recent genome-wide association study (GWAS) has reported significant associations in subgroups of patients [4,5]. Accordingly, environmental factors remain crucial in SSc development and are thought to impact gene expression through epigenetic changes [6–8], particularly DNA methylation, which manifests a partial instability responsible for phenotypic differences across genetic identical organisms [9,10].

An additional clue to SSc pathogenesis comes from its female predominance with a sex ratio as high as 12:1 [11] and from the proposed theories related to X chromosome changes [12]. Peripheral lymphocytes from women with SSc manifest an enhanced rate of X chromosome loss (i.e. X monosomy) [13] and possibly a more frequently skewed X inactivation pattern [14,15], which may contribute to an haplo-insufficiency of X-linked genes predisposing to autoimmunity. Recent experimental evidence suggests that some genes variably escape X chromosome inactivation in women and thus epigenetic differences in X-linked genes could explain both the female preponderance and low monozygotic twin concordance in autoimmune disorders such as SSc [16]. We herein report the first study of the X chromosome-wide DNA methylation profile in the unique model of MZ twins discordant and concordant for SSc. Using this approach, we identify differentially methylated genes that will be useful in dissecting the epigenetic bases of the disease.

Materials and methods

Subjects

Genomic DNA was extracted from peripheral blood mononuclear cells (PBMC) from eight pairs of MZ twins which in seven cases were discordant and in one case concordant for SSc (in the latter case one subject had diffuse and one had limited SSc). The age of discordant twins ranged between 41 and 59 years, while the concordant set was 62 years old at the time of enrolment. Twin sets only included women and have already been described in a previous work, along with the DNA extraction methods [3]. The protocol was approved by the IRB of the University of California at Davis and all subjects provided written informed consent.

Methylated DNA immunoprecipitation (MeDIP)

DNA samples were sheared randomly by sonication to generate fragments between 300 and 500 base pairs (bp), which were immunoprecipitated with a monoclonal mouse antibody against 5-methylcytidine (Ab108005; Abcam, Cambridge, UK). The MeDIP efficiency was verified by polymerase chain reaction (PCR). After the enrichment of MeDIP DNA was validated, genomic MeDIP and control fragments were converted to PCR-amplifiable OmniPlex™ Library (Rubicon Genomics, Ann Arbor, MI, USA) molecules flanked by universal priming sites. The library was then amplified using the Genomeplex Complete WGA 2 Kit (catalogue no. WGA2-50RXN; Sigma, St Louis, MO, USA) by PCR using universal primers with a limited number of cycles.

DNA methylation microarrays

Two to 4 µg of immunoprecipitated and reference DNA were tagged, respectively, with cyanine-5 (Cy5) and Cy3-labelled random 9-mers and hybridized using the NimbleGen Array Hybridization Kit (Roche, Madison, WI, USA). A custom DNA methylation 4-plex array was obtained and utilized to include 998 X chromosome and 18 086 autosomal chromosome promoter sites for methylation analysis for each sample. Oligomers (50–60 nucleotides) used in the microarray hybridization were designed to embrace wide promoter-including regions. The detailed sample preparation protocol is available upon request from Roche Microarray Technical Support.

Data processing and statistical analysis

Our data analysis was limited to the X chromosome sites, but we also report that none of the autosomic chromosome sites met the established consistency criteria for methylation differences (data not shown). Data obtained from Nimblescan software have been processed and converted into a .gff file for each patient containing a P-value for each probe, individuated by a peak start (i.e. the first base of the peak in the chromosome) and a peak end (i.e. the last base of the peak). Because P-values for each twin were distributed in a Gaussian fashion, after the conversion in P-scores (–log₁₀ P-value), we filtered the data set by selecting only the most probably methylated peaks, i.e. with P-score > 1·31 (corresponding to a P-value < 0·05). Next, we have generated a list of methylated sites shared by the concordant twins couple and subsequently determined methylation peaks consistently different in at least three discordant sets, subdivided according to whether sites were exclusively hypermethylated in the affected twins or in healthy twins.

The University of California Santa Cruz (UCSC) human genome browser build hg18 (http://genome.ucsc.edu; [17]) was utilized to enrich the data set with chromosomal and genic localization of each identified peak. Promoters and cytosine–phosphate–guanine (CpG) islands were detected using a window of ± 2 kb of the transcription starting site while gene names and symbols approved by the HUGO Gene Nomenclature Committee (HGNC) were used. Information about the function and products of each identified gene was obtained from bibliographical research and the online Gene Expression Atlas consulting the EMBL-EBI (European Molecular Biology Laboratory–European Bioinformatics Institute) database.

Gene ontology clustering

The genes identified as being differentially methylated in SSc were investigated using an unsupervised analysis for gene ontology information by Ingenuity Pathway Analysis (IPA) software (Ingenuity Systems, http://www.ingenuity.com). IPA is a network analysis program for biological data in human, mouse and rat that is based on integrated data to retrieve the putative interactions of genes of interest into known or proposed networks. To start building networks, IPA queries the Ingenuity Pathways Knowledge Database for interactions between identified ‘Focus Genes’, in our case hyper- or hypomethylated genes, and all other gene objects stored in the knowledge base, to generate a set of networks, with maximum network size of 35 genes/proteins. Networks are displayed graphically as gene/genes products (nodes) and the biological relationships between the nodes (edges). All edges are supported by at least one reference from the literature, or from canonical information stored in the Ingenuity Pathways Knowledge Base. In addition, IPA computes a score for each network according to the fit of the user's set of significant genes. The score, representing the – log(P-value), indicates the likelihood of the Focus Genes in a network from Ingenuity Knowledge Database being found together randomly.

Results

We identified X chromosome sites that were consistently hypermethylated (n = 18, Table 1) and hypomethylated (n = 25, Table 2) in affected twins. Within the 5-kb window sampled for each X-linked gene, most of the differentially methylated regions (DMRs) were located in promoter regions or CpG islands while two hypermethylated (48980151–48980208 and 104355356–104355413) and six hypomethylated (103883076–103883125, 47226194–47226247, 134532227–134532289, 134532327–134532376, 134532427–134532476, 134532627–134532676) DMRs were found downstream of the transcription start sites. In all cases, DMRs were associated with known genes and we noticed that IL1RAPL2 was found in both lists (the two hypermethylated sites are downstream of the hypomethylated one). In some cases, multiple DMRs belong to the same gene (as in the case of hypomethylated peaks 134532227–134532289, 134532327–134532376, 134532427–134532476 and 134532627–134532676 onto gene DDX26B) or a specific site is located in a CpG island of a bidirectional promoter for two different genes (i.e. hypomethylated peak 152712287–152712338 for genes SSR4 and IDH3G). Three hypomethylated peaks are associated with intergenic single-nucleotide polymorphisms (SNP), with peak 13087308–13087357, including SNP rs61677044, peak 13087708–13087757 falling in a region 150 bp downstream from SNP rs16978681, peaks 126140539–126140588 and 126140739–126140788 mapping to a SNP-rich region. Genes identified by the hypermethylated and hypomethylated sites encode for proteins that are illustrated in Tables 3 and 4, respectively. The 26 proteins include transcription factors, membrane and soluble enzymes, surface antigens and translocation proteins while in some cases proteins are currently defined only structurally, but not functionally.

Table 1.

DNA sites that were found hypermethylated in twins with systemic sclerosis (SSc) and that had similar profiles in concordant twins. For each site the start–end location, the chromosome position and the target gene are specified. In some cases, multiple sites have the same target gene

	Start	End	Position	Target gene	Gene part
1	21868249	21868298	Xp22.11	SMS	Promoter
2	21868349	21868398	Same as 1
3	24944225	24944274	Xp21.3	ARX	CpG island
4	24944125	24944174	Same as 3
5	47305466	47305515	Xp11.23	ARAF	CpG island
6	47305566	47305615	Same as 5
7	48980151	48980208	Xp11.23	CCDC22	Body
8	52794239	52794288	Xp11.22	SSX2	Promoter
9	52794039	52794100	Same as 8
10	52793839	52793895	Same as 8
11	71268623	71268672	Xq13.1	RGAG4	CpG island
12	77246607	77246656	Xq21.1	PGK1	CpG island
13	77246607	77246656	Same as 12
14	104355356	104355413	Xq22.3	IL1RAPL2	Body
15	128867440	128867489	Xq25	UTP14A	CpG island
16	148522050	148522099	Xq28	HSFX1	Promoter
17	149487626	149487675	Xq28	MTM1	CpG island
18	153360053	153360102	Xq28	LAGE3	Promoter

CpG: cytosine–guanine–dinucleotide.

Table 2.

DNA sites that were found hypomethylated in twins with systemic sclerosis (SSc) and that had similar profiles in concordant twins. For each site the start–end location, the chromosome position and the target gene are specified. In some cases, multiple sites have the same target gene

	Start	End	Position	Target gene	Gene part
1	103883076	103883125	Xq22.3	IL1RAPL2	Body
2	103883176	103883225	Same as above
3	13087308	13087357	Xp22.2	See results
4	13087708	13087757	Xp22.2	See results
5	126140539	126140588	Xq25	See results
6	126140739	126140788	Same as above
7	2429530	2429579	Xp22.33	ZBED1	Promoter
8	23982786	23982835	Xp22.11	EIF2S3	CpG island
9	41077310	41077359	Xp11.4	DDX3X	CpG island
10	47226194	47226247	Xp11.3	ZNF41	Body
11	118253590	118253639	Xq24	PGRMC1	CpG island
12	118592857	118592906	Xq24	UBE2A	CpG island
13	129230204	129230253	Xq25	ZNF280C	CpG island
14	129864954	129865003	Xq26.1	ENOX2	Promoter
15	133759124	133759176	Xq26.3	FAM122C	Promoter
16	133991162	133991218	Xq26.3	FAM127C	Promoter
17	134532227	134532289	Xq26.3	DDX26B	Body
18	134532327	134532376	Same as 17
19	134532427	134532476	Same as 17
20	134532627	134532676	Same as 17
21	148429003	148429052	Xq28	CXorf40A	Promoter
22	148429303	148429352	Same as 21
23	148429403	148429452	Same as 21
24	152712287	152712338	Xq28	SSR4	CpG island
				IDH3G	CpG island
25	152712387	152712436	Same as above

CpG: cytosine–guanine–dinucleotide.

Table 3.

Proteins encoded by genes identified by hypermethylated sites in affected twins

Gene symbol	Gene name	Localization	Protein type	Function	Cell or tissue expression	Diseases
SMS	Sspermine synthase	Cytoplasm	Enzyme	Arginine and proline metabolism; methionine metabolism; urea cycle and metabolism of amino groups; β-alanine metabolism	Double-negative T cells, bone marrow cell lines, breast carcinoma, gastrocnemius, myeloma cell lines	Mental retardation, X-linked, Snyder–Robinson type, prostatic carcinoma
ARX	Aristaless related homeobox	Nucleus	DNA binding transcription factor	Tissue development, especially of brain	Neural precursor cells, brain, skeletal muscle, heart, liver, testis, Ewing's sarcoma	Lissencephaly X-linked type 2, epileptic encephalopathy early infantile type 1, Partington syndrome, agenesis of the corpus callosum with abnormal genitalia
ARAF	V-raf murine sarcoma 3611 viral oncogene homolog	Cytoplasm (mitochondria, nucleus)	Enzyme	Involved in pathway of EGFR1	Bone marrow, melanoma cell lines, CD34+ cells, acute myeloid leukaemia blast cells, choroidal melanocytes, eye, heart tissue, lymphoblastoid cell lines, squamous cell carcinoma cell lines	–
CCDC22	Coiled-coil domain containing 22	Unknown	Unknown	Regulation apoptosis (?)	Immature dendritic cells, pancreas, stomach; renal, endometrial and ovarian cancer	Over-expression in malignancies
SSX2	Synovial sarcoma, X breakpoint 2	Nucleus	Transcription factor	Transcription regulator activity	Testis, thyroid gland	Synovial sarcoma
RGAG4	Retrotransposon gag domain containing 4	Nucleus	Unknown	Unknown	Ubiquitous	–
PGK1	Phosphoglycerate kinase 1	Cytoplasm (nucleus, mitochondria)	Enzyme	Glycolysis. May also act as a co-factor for polymerase alpha	Ubiquitous	Aemolytic anaemia, rhabdomyolisis
IL1RAPL2	Interleukin 1 receptor accessory protein-like 2	Plasma membrane	Co-receptor of IL-1R (?)	Cell communication, signal transduction (?)	Brain, ovary	–
UTP14A	UTP14, U3 small nucleolar ribonucleoprotein, homolog A (yeast)	Nucleolus	Possible protein cofactor	18S rRNA synthesis. Also binds p53 and promotes p53 degradation	–	–
HSFX1	Heat shock transcription factor family, X linked 1	Nucleus	Transcription factor	Transcription regulator activity	Testis	Male infertility, amyotrophic lateral sclerosis
MTM1	Myotubularin 1	Cytoplasm	Enzyme	Dual-specificity phosphatase that acts on both phosphotyrosine and phosphoserine	Brain, heart, kidney, liver, placenta, lung, pancreas	X-linked myotubular myopathy
LAGE3	L antigen family, member 3	Cytoplasm, nucleus	Unknown	Unknown	Ubiquitous	–

Table 4.

Proteins encoded by genes identified by hypomethylated sites in affected twins

Gene symbol	Gene name	Localization	Protein type	Function	Sites of expression	Diseases
IL1RAPL2	Interleukin 1 receptor accessory protein-like 2	See Table 3
ZBED1	Zinc finger, BED-type containing 1	Nucleus	Transcription factor	Binds to DNA elements found in the promoter regions of several genes related to cell proliferation, such as histone H1	Ubiquitous	–
EIF2S3	Eukaryotic translation initiation factor 2, subunit 3 gamma, 52 kDa	Cytoplasm	Translational factor	Subunit gamma of GTP-binding protein involved in the recruitment of methionyl-tRNA to the 40S ribosomal subunit	Ubiquitous	Neurodegenerative disorders, prostate cancer
DDX3X	DEAD (Asp-Glu-Ala-Asp) box polypeptide 3, X-linked	Nucleus	Enzyme	RNA helicases involved in expression of virus-stimulated Type I interferons	Ubiquitous	Amyotrophic lateral sclerosis
				Cell growth and apoptosis
ZNF41	Zinc finger protein 41	Nucleus	DNA binding protein	Transcription regulator activity	Ubiquitous	Mental retardation
PGRMC1	Progesterone receptor membrane component 1	Plasma membrane	Receptor	Cell viability and death	Endometrium, kidney, liver, monocytes	Tumorigenesis of reproductive tissues, Huntington's disease
UBE2A	Ubiquitin-conjugating enzyme E2A	Nucleus, cytoplasm	Enzyme	Protein degradation	Tumour cell lines, embryonic cell lines, heart tissues, pancreatic tissues, placental tissue	Mental, retardation; UBE2A deficiency syndrome
ZNF280C	Zinc finger protein 280C	Nucleus	Transcription factor (?)
ENOX2	Ecto-NOX disulfphide-thiol exchanger 2	Plasma membrane, excreted	Enzyme	Catalysis of hydroquinone or NADH oxidation, and protein disulphide interchange	Ubiquitous	Serum and cellular marker of tumour progression and body ageing
FAM122C	Family with sequence similarity 122C	Unknown	Unknown	Unknown	–	–
FAM127C	Family with sequence similarity 127, member C	Unknown	Unknown	Unknown	Ubiquitous	–
DDX26B	DEAD/H (Asp-Glu-Ala-Asp/His) box polypeptide 26B	Unknown	Unknown	Unknown (RNA helicase activity?)	–	–
CXorf40A	Chromosome X open reading frame 40A	Cytoplasm, nucleus	Unknown	Involved in cell proliferation and inflammation (?)	Colon, heart, kidney, liver, placenta, small intestine, spleen	Parkinson's disease
SSR4	Signal sequence receptor, delta (translocon-associated protein delta)	Endoplasmic reticulum	Subunit of TRAP complex	Protein translocation through endoplasmic reticulum membrane	Brain, heart, kidney, liver, placenta, lung, pancreas, monocytes, B cells	–
IDH3G	Isocitrate dehydrogenase 3 gamma subinit	Mitochondrion	Enzyme	Reversible decarboxylation of isocitrate to 2-oxoglutarate	Ubiquitous	–

NADH: nicotinamide adenine dinucleotide.

We explored possible functional relationships between the 26 genes using the IPA Knowledge Database. Unsupervised IPA network analysis identified a single cluster of 25 genes that included seven of our 26 genes and 18 additional genes, which was unlikely to occur by chance (P = 10⁻¹³). The plausible biological network generated is shown in Fig. 1 and we note that seven of our 26 genes were gathered in a highly significant network revolving around interleukin 6 (IL-6).

Fig. 1.

Putative gene network derived from Ingenuity Pathway Analysis (IPA) software analysis. Edges are displayed with labels that describe the nature of the relationship between nodes while lines connecting genes represent known interactions (with solid lines representing direct interactions and dashed lines representing indirect interactions). Nodes are displayed using various shapes that represent the functional class of the gene product (see insert). Black highlighting indicates the seven genes differentially methylated in our analysis and non-highlighted genes are those identified by IPA.

Discussion

This is the first report of a genome-wide fine mapping of DNA methylation in MZ twins discordant and concordant for SSc. Interestingly, we found that consistent differences between the studied twins affect only genes located on the X chromosome, thus possibly contributing to the aetiology of SSc female predominance. The study of individual susceptibility to autoimmune diseases is hampered by numerous issues which apply well to SSc, including the rare prevalence, the long latency between the exposure to specific environmental factors and disease onset and the limited applicability of GWAS data gathered in recent studies [18–25]. These limitations are well represented by the variable concordance rates in MZ twins for specific autoimmune diseases and suggest that epigenetic changes may constitute the missing link between individual susceptibility and environmental factors. Data on the epigenetics of SSc are limited to the observation that DNA from CD4⁺ T cell of patients with SSc is hypomethylated significantly compared to healthy controls, along with a reduced expression of enzymes crucial to DNA methylation such as DNMT1, MBD3 and MBD4 [26]. More specifically, the FL1 promoter is down-regulated by CpG methylation in SSc fibroblasts, thus influencing the expression of collagen alpha 1 and other matrix proteins [27]. Conversely, a growing amount of data is being produced by epigenome-wide studies of peripheral blood cells from MZ twins discordant for autoimmune diseases such as type 1 diabetes [28], multiple sclerosis [29], systemic lupus erythematosus [30] and psoriasis [31]. These studies are performed mainly on effector cell subpopulations (monocytes, T cells) to identify differentially expressed genes possibly preceding disease onset [28].

Investigating DMRs in peripheral blood mononuclear cells (PBMC) from MZ twins discordant and concordant for SSc to search for aetiological factors and biomarkers for SSc is expected to be a powerful tool in spite of the limited number of samples examined. First, the limited number of samples should not be considered as a limit of this study based on the low prevalence of the disease in the general population, ranging from 71 to 433 cases per million [32], the low rate of MZ twinning (approximately three to four per 1000 pregnancies) [33] and the low concordance for SSc in such twins [3]; these factors suggest that our series of twins is representative of a general population of 10 million individuals. Secondly, several studies investigated the expression signature in PBMC from patients affected by complex diseases [34–36], including SSc, with reported correlations with defined subsets of SSc and different organ involvements [37]. Among such identified markers, interferon (IFN)-induced protein 44 seems to be one of the most highly differentially expressed gene in SSc monocytes and CD4⁺ T cells as well as IL-1α and IL-16 [38]. While we could not identify genes from this previous report as differentially methylated in MZ twins, our approaches are similar in providing support that peripheral blood is the ideal target tissue for the development of SSc biomarkers.

While our custom-designed array included 998 sites from the X chromosome and several thousand sites from autosomal chromosomes, only X chromosome sites were found to be consistently hypermethylated (n = 18) or hypomethylated (n = 25) in twins with SSc while sharing identical methylation profiles in the one set concordant for the disease. These sites corresponded to 26 genes that were investigated further for their function and their putative pathways involved; such genes could be expected to be expressed differentially based on their methylation profile although an inverse correlation between hypermethylation and reduced expression can only be hypothesized and should be investigated by reverse transcription–polymerase chain reaction (RT–PCR) in the future if additional blood samples can be obtained for this purpose.

Among differentially methylated sites, we found some of particular interest. First, the spermine synthase (SMS) gene encodes for an enzyme involved in polyamine synthesis and recycling [39]. The synthesis is directed by two aminopropyltransferases, i.e. spermidine synthases converting putrescine into spermidine and SMS which converts spermidine into spermine, with decarboxylated S-adenosylmethionine (SAM) as the aminopropyl donor in both cases [40]. A ‘polyamine hypothesis’ has been proposed recently by Wesley Brooks, who suggests that alterations in polyamine synthesis may lead to disease phenotype secondary to abnormalities in DNA methylation status when SAM is in excess [41]. Secondly, among hypomethylated sites, four refer to the gene encoding for DDX26B, a member of the DEAD/H-box helicases involved in various steps of RNA metabolism and chromatin dynamics [42,43], particularly in the Drosophila mode [44]. Of note, DDX proteins are involved in virus-induced innate immunity acting as a scaffold for protein–protein interactions in the signalling cascade that controls IFN type 1 [45] as a critical component of the TANK-binding kinase-1 that activates the transcription factor IFN regulatory factor (IRF)-1 and IRF-7 to promote the expression of IFN-α and -β[46]. Accordingly, changes in DDX3X expression mediated by altered methylation may support the correlation between viral infections and SSc [47]. Thirdly, the association between SSc and ENOX2 hypomethylation is of particular interest, as tissue hypoxia is a major feature of SSc tissues. ENOX2 is a membrane copper-containing enzyme that catalyzes electron transfer from hydroquinone or nicotinamide adenine dinucleotide (NADH) to molecular oxygen, thus playing a role in the induction of reactive oxygen species [48] which have a direct profibrogenic effects on fibroblasts, thus favouring fibrosis [49]. Fourthly, four differentially methylated sites mapped within gene bodies (intragenic methylation) and the process is now recognized to variably affect gene expression, as observed in plants [50,51] and mammals [52–54]. Among these genes, IL1RALP2 belongs to a novel class of IL-1/Toll-like receptor family characterized by the presence of a 150 amino acids carboxy terminus that has no significant homology with any protein of known function [55]. Signalling experiments indicate that neither ILRAPL2 nor its homologue ILRAPL1 can mediate transcriptional activation of nuclear factor (NF)-κB in response to IL-1α, IL-1β or IL-18 [56] and the protein biological features remain to be defined, but a functional study suggests that the ILRAPL1 intracellular domain is crucial to neutrophil function during the inflammatory response [57]. Finally, we were intrigued by the observation that several genes associated with hypomethylated sites have been linked with neurological disorders and mental retardation. While we can only speculate on the putative mechanisms justifying this association, we are traced back to earlier reports of the co-existence of phenylketonuria-associated mental retardation and scleroderma [58]. Further, there are data suggesting that patients with SSc may have disturbances of the nervous system as represented by an impaired response to stress [59], the detection of central nervous system ischaemic vasculopathy at magnetic resonance imaging [60,61] and the changes in cerebrovascular reactivity [62].

To determine the mechanistic implications of our findings we utilized IPA in an unsupervised manner to allow the identification of gene–gene relationship without a priori assumptions. This analysis linked seven of our 26 genes in a highly significant network around IL-6. This is not surprising, as IL-6 seems to be involved in SSc endothelial dysfunction and fibrogenesis [63]. Increased IL-6 levels have been found in serum [64] and lesional skin specimens from patients with SSc [65]. Further, an increased B cell-mediated IL-6 production has been reported from SSc lung fibroblasts in vitro with a parallel increased collagen production, and the pharmacological B cell depletion in SSc leads to a decrease in skin score paralleled by decreased IL-6 serum levels [66]. Finally, SSc serum is able to induce IL-6-mediated endothelial activation and apoptosis [67]. Nevertheless, we should also note that the genes identified in the present work do not include interleukins or their receptors, possibly based on a more indirect effect of other genes [68].

In conclusion, we report herein X chromosome genes that may contribute to SSc susceptibility through differential gene methylation, and thus expression, independent of genomic SNPs or variants. This is particularly relevant provided that data were gathered from MZ twins which recognize an identical genomic sequence. We are aware that expression data and cell subpopulation analysis need to be sought to confirm the proposed associations which could be masked by cumulative cell population effects or be modulated by additional mechanisms which include but are not limited to histone post-translational modifications and non-coding RNAs (such as microRNAs). This limitation is well represented by the lack of changes observed in DNA methylation, possibly leading to different interleukin expression, as reported in SSc peripheral blood [68]. Nevertheless, we are convinced that genome-wide epigenomic studies have the unique potential to provide new evidences on the aetiopathogenesis of complex diseases while possibly proposing novel clinical biomarkers and therapeutic targets.

Disclosure

The authors have nothing to disclose.