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Depression is currently the leading cause of disability around the world. We conducted an epigenome-wide association study (EWAS) in a sample of 58 depression score-discordant monozygotic twin pairs, aiming to detect specific epigenetic variants potentially related to depression and further integrate with gene expression profile data. Association between the methylation level of each CpG site and depression score was tested by applying a linear mixed effect model. Weighted gene co-expression network analysis (WGCNA) was performed for gene expression data. The association of DNA methylation levels of 66 CpG sites with depression score reached the level of P < 1 × 10−4. These top CpG sites were located at 34 genes, especially PTPRN2, HES5, GATA2, PRDM7, and KCNIP1. Many ontology enrichments were highlighted, including Notch signaling pathway, Huntington disease, p53 pathway by glucose deprivation, hedgehog signaling pathway, DNA binding, and nucleic acid metabolic process. We detected 19 differentially methylated regions (DMRs), some of which were located at GRIK2, DGKA, and NIPA2. While integrating with gene expression data, HELZ2, PTPRN2, GATA2, and ZNF624 were differentially expressed. In WGCNA, one specific module was positively correlated with depression score (r = 0.62, P = 0.002). Some common genes (including BMP2, PRDM7, KCNIP1, and GRIK2) and enrichment terms (including complement and coagulation cascades pathway, DNA binding, neuron fate specification, glial cell differentiation, and thyroid gland development) were both identified in methylation analysis and WGCNA. Our study identifies specific epigenetic variations which are significantly involved in regions, functional genes, biological function, and pathways that mediate depression disorder.

Mosaicism in blood varies with age, and cross-sectional studies indicate that for women, skewness of X-chromosomal mosaicism increases with age. This pattern could, however, also be due to less X-inactivation in more recent birth cohorts. Skewed X-chromosome inactivation was here measured longitudinally by the HUMARA assay in 67 septuagenarian and octogenarian women assessed at 2 time points, 10 years apart, and in 10 centenarian women assessed at 2 time points, 2–7 years apart. Skewed X-chromosome inactivation was also compared in 293 age-matched septuagenarian twins born in 1917–1923 and 1931–1937, and 212 centenarians born in 1895, 1905 and 1915. The longitudinal study of septuagenarians and octogenarians revealed that 16% (95% CI 7–29%) of the women developed skewed X-inactivation over a 10-year period. In the cross-sectional across-birth cohort study, the earlier-born septuagenarian (1917–1923) and centenarian women (1895) had a higher degree of skewness than the respective recent age-matched birth cohorts, which indicates that the women in the more recent cohorts, after the age of 70, had not only changed degree of skewness with age, they had also undergone less age-related hematopoietic sub-clone expansion. This may be a result of improved living conditions and better medical treatment in the more recent birth cohorts.

Expression of HOX transcript antisense intergenic RNA ( HOTAIR )—a long non-coding RNA—has been examined in a variety of human cancers, and overexpression of HOTAIR is correlated with poor survival among breast, colon, and liver cancer patients. In this retrospective study, we examine HOTAIR expression in 164 primary breast tumors, from patients who do not receive adjuvant treatment, in a design that is paired with respect to the traditional prognostic markers. We show that HOTAIR expression differs between patients with or without a metastatic endpoint, respectively. Survival analysis shows that high HOTAIR expression in primary tumors is significantly associated with worse prognosis independent of prognostic markers ( P  = 0.012, hazard ratio (HR) 1.747). This association is even stronger when looking only at estrogen receptor (ER)-positive tumor samples ( P  = 0.0086, HR 1.985). In ER-negative tumor samples, we are not able to detect a prognostic value of HOTAIR expression, probably due to the limited sample size. These results are successfully validated in an independent dataset with similar associations ( P  = 0.018, HR 1.825). In conclusion, our findings suggest that HOTAIR expression may serve as an independent biomarker for the prediction of the risk of metastasis in ER-positive breast cancer patients.

ObjectiveBody mass index (BMI) serves as an important measurement of obesity and adiposity, which are highly correlated with cardiometabolic diseases. Although high heritability has been estimated, the identified genetic variants by genetic association studies only explain a small proportion of BMI variation. As an active effort for further exploring the molecular basis of BMI variation, large-scale epigenome-wide association studies have been conducted but with limited number of loci reported, perhaps due to poorly controlled confounding factors, including genetic factors. Being genetically identical, monozygotic twins discordant for BMI are ideal subjects for analyzing the epigenetic association between DNA methylation and BMI, providing perfect control on their genetic makeups largely responsible for BMI variation.SubjectsWe performed an epigenome-wide association study on BMI using 30 identical twin pairs (15 male and 15 female pairs) with age ranging from 39 to 72 years and degree of BMI discordance ranging from 3–7.5 kg/m2. Methylation data from whole blood samples were collected using the reduced representation bisulfite sequencing technique.ResultsAfter adjusting for blood cell composition and clinical variables, we identified 136 CpGs with p-value < 1e-4, 30 CpGs with p < 1e-05 but no CpGs reached genome-wide significance. Genomic region-based analysis found 11 differentially methylated regions harboring coding and non-coding genes some of which were validated by gene expression analysis on independent samples.ConclusionsOur DNA methylation sequencing analysis on identical twins provides new references for the epigenetic regulation on BMI and obesity.

Background The therapeutic management of obesity is challenging, hence further elucidating the underlying mechanisms of obesity development and identifying new diagnostic biomarkers and therapeutic targets are urgent and necessary. Here, we performed differential gene expression analysis and weighted gene co-expression network analysis (WGCNA) to identify significant genes and specific modules related to BMI based on gene expression profile data of 7 discordant monozygotic twins. Results In the differential gene expression analysis, it appeared that 32 differentially expressed genes (DEGs) were with a trend of up-regulation in twins with higher BMI when compared to their siblings. Categories of positive regulation of nitric-oxide synthase biosynthetic process, positive regulation of NF-kappa B import into nucleus, and peroxidase activity were significantly enriched within GO database and NF-kappa B signaling pathway within KEGG database. DEGs of NAMPT , TLR9 , PTGS2 , HBD , and PCSK1N might be associated with obesity. In the WGCNA, among the total 20 distinct co-expression modules identified, coral1 module (68 genes) had the strongest positive correlation with BMI ( r  = 0.56, P  = 0.04) and disease status (r = 0.56, P  = 0.04). Categories of positive regulation of phospholipase activity, high-density lipoprotein particle clearance, chylomicron remnant clearance, reverse cholesterol transport, intermediate-density lipoprotein particle, chylomicron, low-density lipoprotein particle, very-low-density lipoprotein particle, voltage-gated potassium channel complex, cholesterol transporter activity, and neuropeptide hormone activity were significantly enriched within GO database for this module. And alcoholism and cell adhesion molecules pathways were significantly enriched within KEGG database. Several hub genes, such as GAL , ASB9 , NPPB , TBX2 , IL17C , APOE , ABCG4 , and APOC2 were also identified. The module eigengene of saddlebrown module (212 genes) was also significantly correlated with BMI ( r  = 0.56, P  = 0.04), and hub genes of KCNN1 and AQP10 were differentially expressed. Conclusion We identified significant genes and specific modules potentially related to BMI based on the gene expression profile data of monozygotic twins. The findings may help further elucidate the underlying mechanisms of obesity development and provide novel insights to research potential gene biomarkers and signaling pathways for obesity treatment. Further analysis and validation of the findings reported here are important and necessary when more sample size is acquired.

Body mass results from a complex interplay between genetics and environment. Previous studies of the genetic contribution to body mass have excluded repetitive regions due to the technical limitations of platforms used for population scale studies. Here we apply genome-wide approaches, identifying an association between adult body mass and the copy number (CN) of 47S-ribosomal DNA (rDNA). rDNA codes for the 18 S, 5.8 S and 28 S ribosomal RNA (rRNA) components of the ribosome. In mammals, there are hundreds of copies of these genes. Inter-individual variation in the rDNA CN has not previously been associated with a mammalian phenotype. Here, we show that rDNA CN variation associates with post-pubertal growth rate in rats and body mass index in adult humans. rDNA CN is not associated with rRNA transcription rates in adult tissues, suggesting the mechanistic link occurs earlier in development. This aligns with the observation that the association emerges by early adulthood. Many genetic variants have been associated with body size, but the contribution of copy number of rDNA is unknown. Here, the authors explore the association between rDNA copy number and body size in both rats and humans, finding that lower rDNA CN is associated with higher weight and BMI.

Background Cardiovascular diseases (CVDs) are major causes of mortality and morbidity worldwide; yet the understanding of their molecular basis is incomplete. Multi-omics studies have significant potential to uncover these mechanisms, but such studies are challenged by genetic and environmental confounding—a problem that can be effectively reduced by investigating intrapair differences in twins. Here, we linked data on all diagnoses of the circulatory system from the nationwide Danish Patient Registry (spanning 1977–2022) to a study population of 835 twins holding genome-wide DNA methylation and gene expression data. CVD diagnoses were divided into prevalent or incident cases (i.e., occurring before or after blood sample collection (2007–2011)). The diagnoses were classified into four groups: cerebrovascular diseases, coronary artery disease (CAD), arterial and other cardiovascular diseases (AOCDs), and diseases of the veins and lymphatic system. Statistical analyses were performed by linear (prevalent cases) or cox (incident cases) regression analyses at both the individual-level and twin pair-level. Significant genes ( p  < 0.05) in both types of biological data and at both levels were inspected by bioinformatic analyses, including gene set enrichment analysis and interaction network analysis. Results In general, more genes were found for prevalent than for incident cases, and bioinformatic analyses primarily found pathways of the immune system, signal transduction and diseases for prevalent cases, and pathways of cell–cell communication, metabolisms of proteins and RNA, gene expression, and chromatin organization groups for incident cases. This potentially reflects biology related to response to CVD (prevalent cases) and mechanisms related to regulation and development of disease (incident cases). Of specific genes, Myosin 1E was found to be central for CAD, and DEAD-Box Helicase 5 for AOCD. These genes were observed in both the prevalent and the incident analyses, potentially reflecting that their DNA methylation and gene transcription levels change both because of disease (prevalent cases) and prior disease (incident cases). Conclusion We present novel biomarkers for CVD by performing multi-omics analysis in twins, hereby lowering the confounding due to shared genetics and early life environment—a study design that is surprisingly rare in the field of CVD, and where additional studies are highly needed.

Background Prognostic tools for determining patients with indolent breast cancers (BCs) are far from optimal, leading to extensive overtreatment. Several studies have demonstrated mRNAs, lncRNAs and miRNAs to have prognostic potential in BC. Because mRNAs, lncRNAs, and miRNAs capture distinct transcriptomic information, we hypothesized that combining them would improve classification performance. Methods Our pair-matched design study included fresh frozen primary tumor samples from 160 lymph node negative and systemically untreated BC patients of which 80 developed recurrence while 80 remained recurrence-free (mean follow-up of 20.9 years). We integrated three classes of RNA and subsequently performed classification using seven machine learning methods followed by a voting scheme. Results Under the criteria of ≥ 90% sensitivity, individual classifications resulted in specificities ranging from 74–91% for the integrated dataset and 56–66%, 58–71% and 69–86% for mRNAs, lncRNAs and miRNAs individually. The specificity level for the multi-transcriptomic dataset was 85% after voting while it was 38%, 48% and 82% for mRNAs, lncRNAs and miRNAs, respectively. In the clinical setting, very high sensitivity may be requested. In the most stringent clinical setting with a sensitivity of 99%, the integrated dataset also outperformed the others with a specificity of 41% compared to 0%, 9% and 28% for mRNAs, lncRNAs and miRNAs, respectively. Conclusion Our results strongly suggest an improvement of prognostic power for classification using an integrated dataset compared to individual classes of RNA and thus encourage researches to opt for an integration of datasets rather than analyzing them separately.

Background Hypertension is a crucial risk factor for developing cardiovascular disease and reducing life expectancy. We aimed to detect DNA methylation (DNAm) variants potentially related to systolic blood pressure (SBP) and diastolic blood pressure (DBP) by conducting epigenome-wide association studies in 60 and 59 Chinese monozygotic twin pairs, respectively. Methods Genome-wide DNA methylation profiling in whole blood of twins was performed using Reduced Representation Bisulfite Sequencing, yielding 551,447 raw CpGs. Association between DNAm of single CpG and blood pressure was tested by applying generalized estimation equation. Differentially methylated regions (DMRs) were identified by comb-P approach. Inference about Causation through Examination of Familial Confounding was utilized to perform the causal inference. Ontology enrichment analysis was performed using Genomic Regions Enrichment of Annotations Tool. Candidate CpGs were quantified using Sequenom MassARRAY platform in a community population. Weighted gene co-expression network analysis (WGCNA) was conducted using gene expression data. Results The median age of twins was 52 years (95% range 40, 66). For SBP, 31 top CpGs ( p  < 1 × 10 –4 ) and 8 DMRs were identified, with several DMRs within NFATC1 , CADM2 , IRX1 , COL5A1 , and LRAT . For DBP, 43 top CpGs ( p  < 1 × 10 –4 ) and 12 DMRs were identified, with several DMRs within WNT3A , CNOT10 , and DAB2IP . Important pathways, such as Notch signaling pathway, p53 pathway by glucose deprivation, and Wnt signaling pathway, were significantly enriched for SBP and DBP. Causal inference analysis suggested that DNAm at top CpGs within NDE1 , MYH11 , SRRM1P2 , and SMPD4 influenced SBP, while SBP influenced DNAm at CpGs within TNK2 . DNAm at top CpGs within WNT3A influenced DBP, while DBP influenced DNAm at CpGs within GNA14 . Three CpGs mapped to WNT3A and one CpG mapped to COL5A1 were validated in a community population, with a hypermethylated and hypomethylated direction in hypertension cases, respectively. Gene expression analysis by WGCNA further identified some common genes and enrichment terms. Conclusion We detect many DNAm variants that may be associated with blood pressure in whole blood, particularly the loci within WNT3A and COL5A1 . Our findings provide new clues to the epigenetic modification underlying hypertension pathogenesis.

Breast cancer cells exhibit complex karyotypic alterations causing deregulation of numerous genes. Some of these genes are probably causal for cancer formation and local growth whereas others are causal for the various steps of metastasis. In a fraction of tumors deregulation of the same genes might be caused by epigenetic modulations, point mutations or the influence of other genes. We have investigated the relation of gene expression and chromosomal position, using eight datasets including more than 1200 breast tumors, to identify chromosomal regions and candidate genes possibly causal for breast cancer metastasis. By use of “Gene Set Enrichment Analysis” we have ranked chromosomal regions according to their relation to metastasis. Overrepresentation analysis identified regions with increased expression for chromosome 1q41-42, 8q24, 12q14, 16q22, 16q24, 17q12-21.2, 17q21-23, 17q25, 20q11, and 20q13 among metastasizing tumors and reduced gene expression at 1p31-21, 8p22-21, and 14q24. By analysis of genes with extremely imbalanced expression in these regions we identified DIRAS3 at 1p31, PSD3, LPL, EPHX2 at 8p21-22, and FOS at 14q24 as candidate metastasis suppressor genes. Potential metastasis promoting genes includes RECQL4 at 8q24, PRMT7 at 16q22, GINS2 at 16q24, and AURKA at 20q13.