Epigenome-wide association studies for systemic autoimmune diseases: The road behind and the road ahead

Trauma context exerts intergenerational effects on child mental health via DNA methylation

Many people experience traumatic or negative events, but few develop mental health issues as a result. This study investigated whether newborn DNA methylation (DNAm) previously associated with maternal childhood physical abuse by her father affected the child's mental health and physical growth, as well as whether it mediated or moderated developmental outcomes.

METHODS

Study sample (N = 903) and data came from Bristol University's Avon Longitudinal Study of Parents and Children. DNAm was measured in cord blood at birth. DNAm data was preprocessed, normalized, and quality controlled before subsetting to 60 CpG sites of interest from previous research. Linear regression analysis examined newborn DNAm and child development outcome associations. Sobel test examined the mediating relationship between mother's history of childhood abuse by father, newborn targeted gene DNAm of significant CpG sites, and child's mental health and physical growth. Moderation analyses examined the interaction effects between the significant CpG sites and mothers' physical abuse by their fathers on child's mental health and physical growth.

RESULTS

Full cohort analyses showed that newborn DNAm of several different CpG sites associates with separation anxiety, fear, and unhappy/tearful presentations in children aged 6-7 y. Sex-specific associations emerged with boys showing associations with anxiety and fear, and girls showing associations with fear and unhappiness. In boys only, cord blood DNAm mediates the effect of maternal childhood trauma on offspring mental health. No moderation effects emerged.

CONCLUSION

Intergenerational effects of mother's relationship to her abuser present in newborn DNAm associate with 7-year-old child's mental health, show sex-specific effects, and newborn DNAm does mediate maternal childhood trauma effects on offspring mental health in early-life.

Genome-wide DNA methylation sequencing reveals epigenetic features and potential biomarkers of Sjögren syndrome

AIM

Sjögren syndrome (SS) is a slowly progressive, inflammatory, autoimmune disease. The aim of this study was to construct the DNA methylation profiles of whole blood of SS patients and healthy controls (HC), and to explore the role of differentially methylated genes in the pathogenesis of the disease.

METHODS

Whole-genome bisulfite sequencing was performed on three SS patients and four HC. The biological function of genes associated with differentially methylated regions (DMRs) was investigated using Gene Ontology functional analysis and Kyoto Encyclopedia of Genes and Genomes pathway analysis, using network-based key driver analysis (KDA) to find KDA genes. In clinical samples of SS patients and controls, the expression levels of KDA genes were validated by quantitative real-time polymerase chain reaction and immunohistochemical analysis. Moreover, the diagnostic value of KDA genes for SS was confirmed using receiver operating characteristic curves.

RESULTS

We identified 322 DMRs, annotated as 162 associated genes. Six genes were selected via the number of networks of KDA genes. Differential expression of genes such as human leukocyte antigen (HLA) class I, ADAR, and OAS2 was observed in patients' peripheral blood mononuclear cells and the minor salivary glands, which can be used as potential diagnostic biomarkers for SS.

CONCLUSION

Clinical sample validation suggested that HLA class I, ADAR, and OAS2 might play a role in the development of SS. Our study shows epigenetic regulatory mechanisms and potential disease markers associated with SS, which in turn will enable us to identify new therapeutic targets.

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.

Characterizing the properties of bisulfite sequencing data: maximizing power and sensitivity to identify between-group differences in DNA methylation

BACKGROUND

The combination of sodium bisulfite treatment with highly-parallel sequencing is a common method for quantifying DNA methylation across the genome. The power to detect between-group differences in DNA methylation using bisulfite-sequencing approaches is influenced by both experimental (e.g. read depth, missing data and sample size) and biological (e.g. mean level of DNA methylation and difference between groups) parameters. There is, however, no consensus about the optimal thresholds for filtering bisulfite sequencing data with implications for the reproducibility of findings in epigenetic epidemiology.

RESULTS

We used a large reduced representation bisulfite sequencing (RRBS) dataset to assess the distribution of read depth across DNA methylation sites and the extent of missing data. To investigate how various study variables influence power to identify DNA methylation differences between groups, we developed a framework for simulating bisulfite sequencing data. As expected, sequencing read depth, group size, and the magnitude of DNA methylation difference between groups all impacted upon statistical power. The influence on power was not dependent on one specific parameter, but reflected the combination of study-specific variables. As a resource to the community, we have developed a tool, POWEREDBiSeq, which utilizes our simulation framework to predict study-specific power for the identification of DNAm differences between groups, taking into account user-defined read depth filtering parameters and the minimum sample size per group.

CONCLUSIONS

Our data-driven approach highlights the importance of filtering bisulfite-sequencing data by minimum read depth and illustrates how the choice of threshold is influenced by the specific study design and the expected differences between groups being compared. The POWEREDBiSeq tool, which can be applied to different types of bisulfite sequencing data (e.g. RRBS, whole genome bisulfite sequencing (WGBS), targeted bisulfite sequencing and amplicon-based bisulfite sequencing), can help users identify the level of data filtering needed to optimize power and aims to improve the reproducibility of bisulfite sequencing studies.

Impacts of Epigenetic Processes on the Health and Productivity of Livestock

The dynamic changes in the epigenome resulting from the intricate interactions of genetic and environmental factors play crucial roles in individual growth and development. Numerous studies in plants, rodents, and humans have provided evidence of the regulatory roles of epigenetic processes in health and disease. There is increasing pressure to increase livestock production in light of increasing food needs of an expanding human population and environment challenges, but there is limited related epigenetic data on livestock to complement genomic information and support advances in improvement breeding and health management. This review examines the recent discoveries on epigenetic processes due to DNA methylation, histone modification, and chromatin remodeling and their impacts on health and production traits in farm animals, including bovine, swine, sheep, goat, and poultry species. Most of the reports focused on epigenome profiling at the genome-wide or specific genic regions in response to developmental processes, environmental stressors, nutrition, and disease pathogens. The bulk of available data mainly characterized the epigenetic markers in tissues/organs or in relation to traits and detection of epigenetic regulatory mechanisms underlying livestock phenotype diversity. However, available data is inadequate to support gainful exploitation of epigenetic processes for improved animal health and productivity management. Increased research effort, which is vital to elucidate how epigenetic mechanisms affect the health and productivity of livestock, is currently limited due to several factors including lack of adequate analytical tools. In this review, we (1) summarize available evidence of the impacts of epigenetic processes on livestock production and health traits, (2) discuss the application of epigenetics data in livestock production, and (3) present gaps in livestock epigenetics research. Knowledge of the epigenetic factors influencing livestock health and productivity is vital for the management and improvement of livestock productivity.

The Contribution of Genetics and Epigenetics to Our Understanding of Health Disparities in Rheumatic Diseases

Socioeconomic determinants of health are associated with worse outcomes in the rheumatic diseases and contribute significantly to health disparities. However, genetic and epigenetic risk factors may affect different populations disproportionally and further exacerbate health disparities. We discuss the role of genetics and epigenetics to the health disparities observed in rheumatic diseases. We review concepts of population genetics and natural selection, current genome-wide genetic and epigenetic studies of several autoimmune diseases, and environmental exposures associated with disease risk in different populations. To understand how genomics influence health disparities in the rheumatic diseases, further studies in different populations worldwide are needed.

Detection and Comparative Analysis of Methylomic Biomarkers of Rheumatoid Arthritis

Rheumatoid arthritis (RA) is a common autoimmune disorder influenced by both genetic and environmental factors. To investigate possible contributions of DNA methylation to the etiology of RA with minimum confounding genetic heterogeneity, we investigated genome-wide DNA methylation in disease-discordant monozygotic twin pairs. This study hypothesized that methylomic biomarkers might facilitate accurate RA detection. A comprehensive series of biomarker detection algorithms were utilized to find the best methylomic biomarkers for detecting RA patients using the methylomic data of the peripheral blood samples. The best model achieved 100.00% in accuracy (Acc) with 81 methylomic biomarkers and a 10-fold cross-validation (10FCV) strategy. Some of the methylomic biomarkers were experimentally confirmed to be associated with the onset or development of RA. It is also interesting to observe that many of the detected biomarkers were from chromosome Y, supporting the knowledge that RA has a significant gender discrepancy.

Systemic Autoimmune Disease Among Adults Exposed to the September 11, 2001 Terrorist Attack

Objective

Autoimmune disease is an emerging condition among persons exposed to the September 11, 2001 attack on the World Trade Center (WTC). Components of the dust cloud resulting from the collapse of the WTC have been associated with development of a systemic autoimmune disease, as has posttraumatic stress disorder (PTSD). We undertook this study to determine whether dust exposure and PTSD were associated with an increased risk of systemic autoimmune disease in a 9/11‐exposed cohort.

Methods

Among 43,133 WTC Health Registry enrollees, 2,786 self‐reported having a post‐9/11 systemic autoimmune disease. We obtained informed consent to review medical records to validate systemic autoimmune disease diagnoses for 1,041 enrollees. Diagnoses of systemic autoimmune diseases were confirmed by classification criteria, rheumatologist diagnosis, or having been prescribed systemic autoimmune disease medication. Controls were enrollees who denied having an autoimmune disease diagnosis (n = 37,017). We used multivariable log‐binomial regression to examine the association between multiple 9/11 exposures and risk of post‐9/11 systemic autoimmune disease, stratifying by responders (rescue, recovery, and clean‐up workers) and community members (e.g., residents, area workers).

Results

We identified 118 persons with systemic autoimmune disease. Rheumatoid arthritis was most frequent (n = 71), followed by Sjӧgren's syndrome (n = 22), systemic lupus erythematosus (n = 20), myositis (n = 9), mixed connective tissue disease (n = 7), and scleroderma (n = 4). Among 9/11 responders, those with intense dust cloud exposure had almost twice the risk of systemic autoimmune disease (adjusted risk ratio 1.86 [95% confidence interval 1.02–3.40]). Community members with PTSD had a nearly 3‐fold increased risk of systemic autoimmune disease.

Conclusion

Intense dust cloud exposure among responders and PTSD among community members were associated with a statistically significant increased risk of new‐onset systemic autoimmune disease. Clinicians treating 9/11 survivors should be aware of the potential increased risk of systemic autoimmune disease in this population.

Current State of Precision Medicine in Primary Systemic Vasculitides

Precision medicine (PM) is an emerging data-driven health care approach that integrates phenotypic, genomic, epigenetic, and environmental factors unique to an individual. The goal of PM is to facilitate diagnosis, predict effective therapy, and avoid adverse reactions specific for each patient. The forefront of PM is in oncology; nonetheless, it is developing in other fields of medicine, including rheumatology. Recent studies on elucidating the genetic architecture of polygenic and monogenic rheumatological diseases have made PM possible by enabling physicians to customize medical treatment through the incorporation of clinical features and genetic data. For complex inflammatory disorders, the prevailing paradigm is that disease susceptibility is due to additive effects of common reduced-penetrance gene variants and environmental factors. Efforts have been made to calculate cumulative genetic risk score (GRS) and to relate specific susceptibility alleles for use of target therapies. The discovery of rare patients with single-gene high-penetrance mutations informed our understanding of pathways driving systemic inflammation. Here, we review the advances in practicing PM in patients with primary systemic vasculitides (PSVs). We summarize recent genetic studies and discuss current knowledge on the contribution of epigenetic factors and extracellular vesicles (EVs) in disease progression and treatment response. Implementation of PM in PSVs is a developing field that will require analysis of a large cohort of patients to validate data from genomics, transcriptomics, metabolomics, proteomics, and epigenomics studies for accurate disease profiling. This multi-omics approach to study disease pathogeneses should ultimately provide a powerful tool for stratification of patients to receive tailored optimal therapies and for monitoring their disease activity.

Interferons (IFN-A/-B/-G) Genetic Variants in Patients with Mixed Connective Tissue Disease (MCTD)

Mixed connective tissue disease (MCTD) is a rare complex autoimmune disease in which autoantigens are recognized by endosomal TLRs. Their activation induces a higher secretion of the type I interferons, IFN-γ and the up-regulation of the INF-inducible genes. The present study aimed to investigate whether SNPs that are located in the IFN-A, IFN-B, and IFN-G genes are associated with MCTD. 145 MCTD patients and 281 healthy subjects were examined for IFN-A, IFN-B, and IFN-G genetic variants by TaqMan SNP genotyping assay. ELISA determined IFN-α/-β/-γ serum levels. Among the seven tested SNPs, four polymorphisms: IFN-A rs10757212, IFN-A rs3758236, IFN-G rs2069705, IFN-G rs2069718, as well as INF-G rs1861493A/rs2069705A/rs2069718G haplotype were significantly associated with a predisposition for MCTD. Raynaud's phenomenon, erosive arthritis, swollen hands and fingers, and sclerodactyly were significantly more frequently observed in MCTD patients with IFN-G rs2069718 G allele than in patients with IFN-G rs2069718 A allele. We also found that anti-U1-A autoantibodies most frequently occurred in MCTD patients with rs2069718 GA genotype, while the IFN-G rs2069705 AG and rs2069718 GA genotypes might be a marker of anti-Ro60 presence in MCTD patients. Our results indicate that IFN-G genetic variants may be potential genetic biomarkers for MCTD susceptibility and severity.

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 Sjögren'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.

Shared and Unique Patterns of DNA Methylation in Systemic Lupus Erythematosus and Primary Sjögren's Syndrome

Objectives: To perform a cross-comparative analysis of DNA methylation in patients with systemic lupus erythematosus (SLE), patients with primary Sjögren's syndrome (pSS), and healthy controls addressing the question of epigenetic sharing and aiming to detect disease-specific alterations.

Methods: DNA extracted from peripheral blood from 347 cases with SLE, 100 cases with pSS, and 400 healthy controls were analyzed on the Human Methylation 450k array, targeting 485,000 CpG sites across the genome. A linear regression model including age, sex, and blood cell type distribution as covariates was fitted, and association p-values were Bonferroni corrected. A random forest machine learning classifier was designed for prediction of disease status based on DNA methylation data.

Results: We established a combined set of 4,945 shared differentially methylated CpG sites (DMCs) in SLE and pSS compared to controls. In pSS, hypomethylation at type I interferon induced genes was mainly driven by patients who were positive for Ro/SSA and/or La/SSB autoantibodies. Analysis of differential methylation between SLE and pSS identified 2,244 DMCs with a majority of sites showing decreased methylation in SLE compared to pSS. The random forest classifier demonstrated good performance in discerning between disease status with an area under the curve (AUC) between 0.83 and 0.96.

Conclusions: The majority of differential DNA methylation is shared between SLE and pSS, however, important quantitative differences exist. Our data highlight neutrophil dysregulation as a shared mechanism, emphasizing the role of neutrophils in the pathogenesis of systemic autoimmune diseases. The current study provides evidence for genes and molecular pathways driving common and disease-specific pathogenic mechanisms.

Aberrant Expressions of Co-stimulatory and Co-inhibitory Molecules in Autoimmune Diseases

Co-signaling molecules include co-stimulatory and co-inhibitory molecules and play important roles in modulating immune responses. The roles of co-signaling molecules in autoimmune diseases have not been clearly defined. We assessed the expressions of co-stimulatory and co-inhibitory molecules in autoimmune diseases through a bioinformatics-based study. By using datasets of whole-genome transcriptome, the expressions of 54 co-stimulatory or co-inhibitory genes in common autoimmune diseases were analyzed using Robust rank aggregation (RRA) method. Nineteen array datasets and 6 RNA-seq datasets were included in the RRA discovery study and RRA validation study, respectively. Significant genes were further validated in several autoimmune diseases including Graves' disease (GD). RRA discovery study suggested that CD160 was the most significant gene aberrantly expressed in autoimmune diseases (Adjusted P = 5.9E-12), followed by CD58 (Adjusted P = 5.7E-06) and CD244 (Adjusted P = 9.5E-05). RRA validation study also identified CD160 as the most significant gene aberrantly expressed in autoimmune diseases (Adjusted P = 5.9E-09). We further found that the aberrant expression of CD160 was statistically significant in multiple autoimmune diseases including GD (P < 0.05), and CD160 had a moderate role in diagnosing those autoimmune diseases. Flow cytometry confirmed that CD160 was differentially expressed on the surface of CD8⁺ T cells between GD patients and healthy controls (P = 0.002), which proved the aberrant expression of CD160 in GD at the protein level. This study suggests that CD160 is the most significant co-signaling gene aberrantly expressed in autoimmune diseases. Treatment strategy targeting CD160-related pathway may be promising for the therapy of autoimmune diseases.