Epigenome-Wide Comparative Study Reveals Key Differences Between Mixed Connective Tissue Disease and Related Systemic Autoimmune Diseases

Mixed Connective Tissue Disease (MCTD) is a rare complex systemic autoimmune disease (SAD) characterized by the presence of increased levels of anti-U1 ribonucleoprotein autoantibodies and signs and symptoms that resemble other SADs such as systemic sclerosis (SSc), rheumatoid arthritis (RA), and systemic lupus erythematosus (SLE). Due to its low prevalence, this disease has been very poorly studied at the molecular level. We performed for the first time an epigenome-wide association study interrogating DNA methylation data obtained with the Infinium MethylationEPIC array from whole blood samples in 31 patients diagnosed with MCTD and 255 healthy subjects. We observed a pervasive hypomethylation involving 170 genes enriched for immune-related function such as those involved in type I interferon signaling pathways or in negative regulation of viral genome replication. We mostly identified epigenetic signals at genes previously implicated in other SADs, for example MX1, PARP9, DDX60, or IFI44L, for which we also observed that MCTD patients exhibit higher DNA methylation variability compared with controls, suggesting that these sites might be involved in plastic immune responses that are relevant to the disease. Through methylation quantitative trait locus (meQTL) analysis we identified widespread local genetic effects influencing DNA methylation variability at MCTD-associated sites. Interestingly, for IRF7, IFI44 genes, and the HLA region we have evidence that they could be exerting a genetic risk on MCTD mediated through DNA methylation changes. Comparison of MCTD-associated epigenome with patients diagnosed with SLE, or Sjogren's Syndrome, reveals a common interferon-related epigenetic signature, however we find substantial epigenetic differences when compared with patients diagnosed with rheumatoid arthritis and systemic sclerosis. Furthermore, we show that MCTD-associated CpGs are potential epigenetic biomarkers with high diagnostic value. Our study serves to reveal new genes and pathways involved in MCTD, to illustrate the important role of epigenetic modifications in MCTD pathology, in mediating the interaction between different genetic and environmental MCTD risk factors, and as potential biomarkers of SADs

Investigating the epigenetic discrimination of identical twins using buccal swabs, saliva, and cigarette butts in the forensic setting

Monozygotic (MZ) twins are typically indistinguishable via forensic DNA profiling. Recently, we demonstrated that epigenetic differentiation of MZ twins is feasible; however, proportions of twin differentially methylated CpG sites (tDMSs) identified in reference-type blood DNA were not replicated in trace-type blood DNA. Here we investigated buccal swabs as typical forensic reference material, and saliva and cigarette butts as commonly encountered forensic trace materials. As an analog to a forensic case, we analyzed one MZ twin pair. Epigenome-wide microarray analysis in reference-type buccal DNA revealed 25 candidate tDMSs with >0.5 twin-to-twin differences. MethyLight quantitative PCR (qPCR) of 22 selected tDMSs in trace-type DNA revealed in saliva DNA that six tDMSs (27.3%) had >0.1 twin-to-twin differences, seven (31.8%) had smaller (<0.1) but robustly detected differences, whereas for nine (40.9%) the differences were in the opposite direction relative to the microarray data; for cigarette butt DNA, results were 50%, 22.7%, and 27.3%, respectively. The discrepancies between reference-type and trace-type DNA outcomes can be explained by cell composition differences, method-to-method variation, and other technical reasons including bisulfite conversion inefficiency. Our study highlights the importance of the DNA source and that careful characterization of biological and technical effects is needed before epigenetic MZ twin differentiation is applicable in forensic casework

Expression Quantitative Trait Locus Analysis in Systemic Sclerosis Identifies New Candidate Genes Associated With Multiple Aspects of Disease Pathology

Objective: To identify the genetic variants that affect gene expression (expression quantitative trait loci [eQTLs]) in systemic sclerosis (SSc) and to investigate their role in the pathogenesis of the disease. Methods: We performed an eQTL analysis using whole-blood sequencing data from 333 SSc patients and 524 controls and integrated them with SSc genome-wide association study (GWAS) data. We integrated our findings from expression modeling, differential expression analysis, and transcription factor binding site enrichment with key clinical features of SSc. Results: We detected 49,123 validated cis-eQTLs from 4,539 SSc-associated single-nucleotide polymorphisms (SNPs) (PGWAS 0.05). As a result, 233 candidates were identified, 134 (58%) of them associated with hallmarks of SSc and 105 (45%) of them differentially expressed in the blood cells, skin, or lung tissue of SSc patients. Transcription factor binding site analysis revealed enriched motifs of 24 transcription factors (5%) among SSc eQTLs, 5 of which were found to be differentially regulated in the blood cells (ELF1 and MGA), skin (KLF4 and ID4), and lungs (TBX4) of SSc patients. Ten candidate genes (4%) can be targeted by approved medications for immune-mediated diseases, of which only 3 have been tested in clinical trials in patients with SSc. Conclusion: The findings of the present study indicate a new layer to the molecular complexity of SSc, contributing to a better understanding of the pathogenesis of the disease

Analysis of ancestral and functionally relevant CD5 variants in systemic lupus erythematosus patients

OBJECTIVE: CD5 plays a crucial role in autoimmunity and is a well-established genetic risk factor of developing RA. Recently, evidence of positive selection has been provided for the CD5 Pro224-Val471 haplotype in East Asian populations. The aim of the present work was to further analyze the functional relevance of non-synonymous CD5 polymorphisms conforming the ancestral and the newly derived haplotypes (Pro224-Ala471 and Pro224-Val471, respectively) as well as to investigate the potential role of CD5 on the development of SLE and/or SLE nephritis. METHODS: The CD5 SNPs rs2241002 (C/T; Pro224Leu) and rs2229177 (C/T; Ala471Val) were genotyped using TaqMan allelic discrimination assays in a total of 1,324 controls and 681 SLE patients of Spanish origin. In vitro analysis of CD3-mediated T cell proliferative and cytokine response profiles of healthy volunteers homozygous for the above mentioned CD5 haplotypes were also analyzed. RESULTS: T-cell proliferation and cytokine release were significantly increased showing a bias towards to a Th2 profile after CD3 cross-linking of peripheral mononuclear cells from healthy individuals homozygous for the ancestral Pro224-Ala471 (CC) haplotype, compared to the more recently derived Pro224-Val471 (CT). The same allelic combination was statistically associated with Lupus nephritis. CONCLUSION: The ancestral Ala471 CD5 allele confers lymphocyte hyper-responsiveness to TCR/CD3 cross-linking and is associated with nephritis in SLE patients

Smoking induces coordinated DNA methylation and gene expression changes in adipose tissue with consequences for metabolic health

Abstract Background Tobacco smoking is a risk factor for multiple diseases, including cardiovascular disease and diabetes. Many smoking-associated signals have been detected in the blood methylome, but the extent to which these changes are widespread to metabolically relevant tissues, and impact gene expression or metabolic health, remains unclear. Methods We investigated smoking-associated DNA methylation and gene expression variation in adipose tissue biopsies from 542 healthy female twins. Replication, tissue specificity, and longitudinal stability of the smoking-associated effects were explored in additional adipose, blood, skin, and lung samples. We characterized the impact of adipose tissue smoking methylation and expression signals on metabolic disease risk phenotypes, including visceral fat. Results We identified 42 smoking-methylation and 42 smoking-expression signals, where five genes (AHRR, CYP1A1, CYP1B1, CYTL1, F2RL3) were both hypo-methylated and upregulated in current smokers. CYP1A1 gene expression achieved 95% prediction performance of current smoking status. We validated and replicated a proportion of the signals in additional primary tissue samples, identifying tissue-shared effects. Smoking leaves systemic imprints on DNA methylation after smoking cessation, with stronger but shorter-lived effects on gene expression. Metabolic disease risk traits such as visceral fat and android-to-gynoid ratio showed association with methylation at smoking markers with functional impacts on expression, such as CYP1A1, and at tissue-shared smoking signals, such as NOTCH1. At smoking-signals, BHLHE40 and AHRR DNA methylation and gene expression levels in current smokers were predictive of future gain in visceral fat upon smoking cessation. Conclusions Our results provide the first comprehensive characterization of coordinated DNA methylation and gene expression markers of smoking in adipose tissue. The findings relate to human metabolic health and give insights into understanding the widespread health consequence of smoking outside of the lung

Integrative Analysis Reveals a Molecular Stratification of Systemic Autoimmune Diseases

Objective Clinical heterogeneity, a hallmark of systemic autoimmune diseases, impedes early diagnosis and effective treatment, issues that may be addressed if patients could be classified into groups defined by molecular pattern. This study was undertaken to identify molecular clusters for reclassifying systemic autoimmune diseases independently of clinical diagnosis. Methods Unsupervised clustering of integrated whole blood transcriptome and methylome cross-sectional data on 955 patients with 7 systemic autoimmune diseases and 267 healthy controls was undertaken. In addition, an inception cohort was prospectively followed up for 6 or 14 months to validate the results and analyze whether or not cluster assignment changed over time. Results Four clusters were identified and validated. Three were pathologic, representing “inflammatory,” “lymphoid,” and “interferon” patterns. Each included all diagnoses and was defined by genetic, clinical, serologic, and cellular features. A fourth cluster with no specific molecular pattern was associated with low disease activity and included healthy controls. A longitudinal and independent inception cohort showed a relapse–remission pattern, where patients remained in their pathologic cluster, moving only to the healthy one, thus showing that the molecular clusters remained stable over time and that single pathogenic molecular signatures characterized each individual patient. Conclusion Patients with systemic autoimmune diseases can be jointly stratified into 3 stable disease clusters with specific molecular patterns differentiating different molecular disease mechanisms. These results have important implications for future clinical trials and the study of nonresponse to therapy, marking a paradigm shift in our view of systemic autoimmune diseases

Genomic and phenotypic insights from an atlas of genetic effects on DNA methylation.

Characterizing genetic influences on DNA methylation (DNAm) provides an opportunity to understand mechanisms underpinning gene regulation and disease. In the present study, we describe results of DNAm quantitative trait locus (mQTL) analyses on 32,851 participants, identifying genetic variants associated with DNAm at 420,509 DNAm sites in blood. We present a database of >270,000 independent mQTLs, of which 8.5% comprise long-range (trans) associations. Identified mQTL associations explain 15–17% of the additive genetic variance of DNAm. We show that the genetic architecture of DNAm levels is highly polygenic. Using shared genetic control between distal DNAm sites, we constructed networks, identifying 405 discrete genomic communities enriched for genomic annotations and complex traits. Shared genetic variants are associated with both DNAm levels and complex diseases, but only in a minority of cases do these associations reflect causal relationships from DNAm to trait or vice versa, indicating a more complex genotype–phenotype map than previously anticipated.C.L.R., G.D.S., G.S., J.L.M., K.B., M. Suderman, T.G.R. and T.R.G. are supported by the UK Medical Research Council (MRC) Integrative Epidemiology Unit at the University of Bristol (MC_UU_00011/1, MC_UU_00011/4, MC_UU_00011/5). C.L.R. receives support from a Cancer Research UK Programme grant (no. C18281/A191169). G.H. is funded by the Wellcome Trust and the Royal Society (208806/Z/17/Z). E.H. and J.M. were supported by MRC project grants (nos. MR/K013807/1 and MR/R005176/1 to J.M.) and an MRC Clinical Infrastructure award (no. MR/M008924/1 to J.M.). B.T.H. is supported by the Netherlands CardioVascular Research Initiative (the Dutch Heart Foundation, Dutch Federation of University Medical Centres, the Netherlands Organisation for Health Research and Development, and the Royal Netherlands Academy of Sciences) for the GENIUS project ‘Generating the best evidence-based pharmaceutical targets for atherosclerosis’ (CVON2011-19, CVON2017-20). J.T.B. was supported by the Economic and Social Research Council (grant no. ES/N000404/1). The present study was also supported by JPI HDHL-funded DIMENSION project (administered by the BBSRC UK, grant no. BB/S020845/1 to J.T.B., and by ZonMW the Netherlands, grant no. 529051021 to B.T.H). A.D.B. has been supported by a Wellcome Trust PhD Training Fellowship for Clinicians and the Edinburgh Clinical Academic Track programme (204979/Z/16/Z). J. Klughammer was supported by a DOC fellowship of the Austrian Academy of Sciences. Cohort-specific acknowledgements and funding are presented in the Supplementary Note

Integrative epigenomics in Sjögren´s syndrome reveals novel pathways and a strong interaction between the HLA, autoantibodies and the interferon signature

Primary Sjögren's syndrome (SS) is a systemic autoimmune disease characterized by lymphocytic infiltration and damage of exocrine salivary and lacrimal glands. The etiology of SS is complex with environmental triggers and genetic factors involved. By conducting an integrated multi-omics study, we confirmed a vast coordinated hypomethylation and overexpression effects in IFN-related genes, what is known as the IFN signature. Stratified and conditional analyses suggest a strong interaction between SS-associated HLA genetic variation and the presence of Anti-Ro/SSA autoantibodies in driving the IFN epigenetic signature and determining SS. We report a novel epigenetic signature characterized by increased DNA methylation levels in a large number of genes enriched in pathways such as collagen metabolism and extracellular matrix organization. We identified potential new genetic variants associated with SS that might mediate their risk by altering DNA methylation or gene expression patterns, as well as disease-interacting genetic variants that exhibit regulatory function only in the SS population. Our study sheds new light on the interaction between genetics, autoantibody profiles, DNA methylation and gene expression in SS, and contributes to elucidate the genetic architecture of gene regulation in an autoimmune population

An integrative cross-omics analysis of DNA methylation sites of glucose and insulin homeostasis

Despite existing reports on differential DNA methylation in type 2 diabetes (T2D) and obesity, our understanding of its functional relevance remains limited. Here we show the effect of differential methylation in the early phases of T2D pathology by a blood-based epigenome-wide association study of 4808 non-diabetic Europeans in the discovery phase and 11,750 individuals in the replication. We identify CpGs in LETM1, RBM20, IRS2, MAN2A2 and the 1q25.3 region associated with fasting insulin, and in FCRL6, SLAMF1, APOBEC3H and the 15q26.1 region with fasting glucose. In silico cross-omics analyses highlight the role of differential methylation in the crosstalk between the adaptive immune system and glucose homeostasis. The differential methylation explains at least 16.9% of the association between obesity and insulin. Our study sheds light on the biological interactions between genetic variants driving differential methylation and gene expression in the early pathogenesis of T2D