Kaikki aineistot
Lisää
The loss of estrogen during menopause causes changes in the female body, with wide-ranging effects on health. Estrogen-containing hormone replacement therapy (HRT) leads to a relief of typical menopausal symptoms, benefits bone and muscle health, and is associated with tissue-specific gene expression profiles. As gene expression is controlled by epigenetic factors (including DNA methylation), many of which are environmentally sensitive, it is plausible that at least part of the HRT-associated gene expression is due to changes in DNA methylation profile. We investigated genome-wide DNA methylation and gene expression patterns of white blood cells (WBCs) and their associations with body composition, including muscle and bone measures of monozygotic (MZ) female twin pairs discordant for HRT. We identified 7,855 nominally significant differentially methylated regions (DMRs) associated with 4,044 genes. Of the genes with DMRs, five (ACBA1, CCL5, FASLG, PPP2R2B, and UHRF1) were also differentially expressed. All have been previously associated with HRT or estrogenic regulation, but not with HRT-associated DNA methylation. All five genes were associated with bone mineral content (BMC), and ABCA1, FASLG, and UHRF1 were also associated with body adiposity. Our study is the first to show that HRT associates with genome-wide DNA methylation alterations in WBCs. Moreover, we show that five differentially expressed genes with DMRs associate with clinical measures, including body fat percentage, lean body mass, bone mass, and blood lipids. Our results indicate that at least part of the known beneficial HRT effects on body composition and bone mass may be regulated by DNA methylation associated alterations in gene expression in circulating WBCs.
Genomic analyses have redefined the molecular subgrouping of pediatric acute lymphoblastic leukemia (ALL). Molecular subgroups guide risk-stratification and targeted therapies, but outcomes of recently identified subtypes are often unclear, owing to limited cases with comprehensive profiling and cross-protocol studies. We developed a machine learning tool (ALLIUM) for the molecular subclassification of ALL in retrospective cohorts as well as for up-front diagnostics. ALLIUM uses DNA methylation and gene expression data from 1131 Nordic ALL patients to predict 17 ALL subtypes with high accuracy. ALLIUM was used to revise and verify the molecular subtype of 281 B-cell precursor ALL (BCP-ALL) cases with previously undefined molecular phenotype, resulting in a single revised subtype for 81.5% of these cases. Our study shows the power of combining DNA methylation and gene expression data for resolving ALL subtypes and provides a comprehensive population-based retrospective cohort study of molecular subtype frequencies in the Nordic countries.
Abstract Reduced cardiac vagal control reflected in low heart rate variability (HRV) is associated with greater risks for cardiac morbidity and mortality. In two-stage meta-analyses of genome-wide association studies for three HRV traits in up to 53,174 individuals of European ancestry, we detect 17 genome-wide significant SNPs in eight loci. HRV SNPs tag non-synonymous SNPs (in NDUFA11 and KIAA1755), expression quantitative trait loci (eQTLs) (influencing GNG11, RGS6 and NEO1), or are located in genes preferentially expressed in the sinoatrial node (GNG11, RGS6 and HCN4). Genetic risk scores account for 0.9 to 2.6% of the HRV variance. Significant genetic correlation is found for HRV with heart rate (-0.74<rg<-0.55) and blood pressure (-0.35<rg<-0.20). These findings provide clinically relevant biological insight into heritable variation in vagal heart rhythm regulation, with a key role for genetic variants (GNG11, RGS6) that influence G-protein heterotrimer action in GIRK-channel induced pacemaker membrane hyperpolarization.