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4,916 results for “DNA methylation”
Title: Health-Related Quality of Life and DNA Methylation-Based Aging Biomarkers among Survivors of Childhood Cancer
<p><strong><span>Abstract</span></strong></p> <p><strong><span>Background: </span></strong><span>Childhood cancer survivors are at high risk for morbidity and mortality and poor patient-reported outcomes, typically health-related-quality-of-life (HRQOL). However, </span><span>associations between DNA methylation (DNAm)-based aging biomarkers and HRQOL have not been evaluated.</span></p> <p><strong><span>Methods: </span></strong><span>DNAm was generated with Infinium EPIC BeadChip on blood-derived DNA (median[range] for age at blood draw=34.5[18.5-66.6] years) and HRQOL was assessed with age at survey (32.3[18.4-64.5] years) from 2,206 survivors in the St. Jude Lifetime Cohort. DNAm-based aging biomarkers, including epigenetic age using multiple clocks (e.g., <span>GrimAge</span>) and others (e.g., DNAmB2M: beta-2-microglobulin; DNAmADM: adrenomedullin), were derived from the DNAm Age Calculator (https://dnamage.genetics.ucla.edu). HRQOL was assessed using the Medical Outcomes Study 36-Item Short-Form Health Survey to capture eight domains, and physical and mental component summaries (PCS and MCS). General linear models evaluated associations between HRQOL and epigenetic age acceleration (EAA, e.g., EAA_GrimAge) or other age-adjusted DNAm-based biomarkers (e.g., ageadj_DNAmB2M) after adjusting for age at blood draw, sex, cancer treatments, and DNAm-based surrogate for smoking pack-years. All P values were 2-sided.</span></p> <p><strong><span>Results: </span></strong><span>Worse HRQOL was associated with greater EAA_GrimAge (</span><span>PCS: β[95%CI]=-0.18[-0.251,-0.11] years, P=1.85×10<sup>-5</sup>; and four individual HRQOL domains), followed by ageadj_DNAmB2M (PCS: -0.08[-0.124,-0.037], P=0.003; and three individual HRQOL domains), and ageadj_DNAmADM (PCS: -0.082[-0.125,-0.039], P=0.002; and two HRQOL domains). EAA_Hannum (Hannum clock) was not associated with any HRQOL.</span></p> <p><strong>Conclustion: </strong>Overall and domain-specific measures of HRQOL are associated with DNAm measures of biological aging. Future longitudinal studies should test biological aging as a potential mechanism.</p>
DNA methylation in sperm of rats at two ages exposed or not to 2,2',4,4'-tetrabromodiphenyl ether
<p><strong>Introduction</strong></p> <p>This study was designed to determine the potential of environmentally relevant levels of 2,2',4,4'-tetrabromodiphenyl ether (BDE-47) to induce age-dependent changes in a rat’s sperm epigenome. The methods used to generate the files are described below.</p> <p> </p> <p><strong>Experimental design</strong></p> <p>Twelve seven-week-old pregnant Wistar rats were divided into two groups (6 per group) – control and BDE-47 exposed group. Between pregnancy day 8 and postnatal day 21 (PND21), dams in each group were fed from the tip of pipette 0.2 µL/g body weight of the vehicle (tocopherol-stripped corn oil) or the same volume of a 1 mg/mL solution of BDE-47 daily. The BDE-47 group resulted in an exposure level of 0.2 mg/kg body weight of BDE-47 per day. On PND65 and PND120, one male pup randomly selected from each litter was euthanized, and epididymal motile spermatozoa were collected via the swim-up procedure as described in detail elsewhere (Suvorov et al., 2018). Sperm DNA was extracted using the rapid method (Wu et al., 2015). Extracted sperm DNA was subjected to reduced representation bisulfite sequencing (RRBS).</p> <p> </p> <p><strong>Reduced representation bisulfite sequencing</strong></p> <p>For RRBS, bisulfite-converted libraries were prepared from 100 ng of the sperm DNA using Ovation RRBS Methyl-Seq System and EpiTect Fast DNA Bisulfite Kit (Cat. #59824, Qiagen) following manufacturers’ protocols. Sequencing of libraries was done using the HiSeq 2500 sequencing system (Illumina) in Deep Sequencing Core Facility of the University of Massachusetts Medical School (Schrewsbury, MA) with an average of 18.0 million unique reads per sample.</p> <p> </p> <p><strong>Bioinformatic analysis</strong></p> <p>Raw reads from the sequence were processed following the recommended protocol for libraries prepared with Ovation RRBS Methyl-Seq System (NuGEN) and then mapped to the rn6 Rattus norvegicus reference genome using Bismark (version 0.16.1) and bowtie-2 (version 2.2.9). PCR duplicates were removed using nudup.py (version 2.2). The resulting SAM files for the control and BDE-47 group for the two ages, PND65 and 120 are uploaded. </p> <p> </p>
DNA methylation in clonal Duckweed lineages (Lemna minor L.) reflects current and historical environmental exposures.
<p>The following depository contains raw phenotypic data and intermediate DNA methylation data presented in the article <strong>"DNA methylation in clonal Duckweed lineages (<em>Lemna minor </em>L.) reflects current and historical environmental exposures.</strong>" :</p> <p><strong>1) Raw phenotypic data</strong></p> <p>- Frond_area_Phase1_Phase2 -> Frond area measured at different time points (week 1, 6, 7 and 8) during the experiment.</p> <p>- Frond_number_Phase1_Phase2 -> Frond number measured at different time points (week 1, 6, 7 and 8) during the experiment.</p> <p><strong>2) Intermediate files obtained from running the epiGBS2 pipeline. The following files are available:</strong></p> <p>- consensus_cluster.renamed.fa -> epiGBS <em>de novo </em>loci. This file consists of the <em>de novo </em>epiGBS reference sequence file obtained during the <em>de novo </em>reference creation.</p> <p>- methylation.filtMETH -> The filtered DNA methylation data. This data was obtained after filtering the raw DNA methylation data. Cytosines which had a 10X coverage or higher and which were present in 80% of all samples were kept for further analysis.</p> <p>Demultiplexed and raw data were deposited at NCBI: BioProject: <strong>PRJNA883550</strong></p>
Data for fitness analyses used in: Environmentally-induced DNA methylation is inherited across generations in water fleas (Daphnia magna)
<p><span>Data of</span> fitness effects of environmental stressors on <em>Daphnia magna</em> over multiple generations. Ages of first and second reproduction, and sizes of first and second brood were measured and used to calculate replacement rate. This data is part of a study on whole-genome bisulphate sequencing on individual <em>Daphnia magna</em> to assess whether environmentally-induced DNA methylation can persist for up to four generations.</p>
The impact of low input DNA on the reliability of DNA methylation as measured by the Illumina Infinium MethylationEPIC BeadChip, supplementary table 3
<p>Supplementary table 3: Summary statistics from an EWAS assessing the relationship between variance in DNA methylation value and DNA input level.</p>
Parent-of-origin detection and chromosome-scale haplotyping using long-read DNA methylation sequencing and Strand-seq
<p>Hundreds of loci in human genomes have alleles that are methylated differentially according to their parent of origin. These imprinted loci generally show little variation across tissues, individuals, and populations. We show that such loci can be used to distinguish the maternal and paternal homologs for all autosomes, without the need for the parental DNA. We integrate methylation-detecting nanopore sequencing with the long-range phase information in Strand-seq data to determine the parent of origin of chromosome-length haplotypes for both DNA sequence and DNA methylation in five trios with diverse genetic backgrounds.</p>
A plant virus differentially alters DNA methylation in two cryptic species of a hemipteran vector
<p>This study investigated DNA methylation patterns in two cryptic species (B and Q) of the sweet potato whitefly, <em>Bemisia tabaci</em> (Gennadius), following the acquisition of the tomato yellow curl virus, a single-stranded DNA virus. The methylation levels in genomic features such as promoters, gene bodies, and transposable elements in both cryptic species were described in this study. While overall trends were found to be similar, specific differences in methylation levels were observed. Virus-induced differentially methylated regions (DMRs) were associated with different genes in each cryptic species and were negatively correlated with differential gene expression. These DMRs were analyzed for changes in gene expression and alternative splicing, revealing clusters of hyper- and hypomethylated genes related to virus-vector interactions, immune functions, and detoxification processes. These methylation differences may help explain the distinct biological and physiological traits observed between the B and Q cryptic species.</p>
Maternal smoking DNA methylation risk score associated with health outcomes in offspring of European and South Asian ancestry
<p>These are a collection of EWAS summary statistics for the following publication:</p> <p>Deng Wei Q, Cawte Nathan, Campbell Natalie, Azab Sandi M, de Souza Russell J, Lamri Amel, Morrison Katherine M, Atkinson Stephanie A, Subbarao Padmaja, Turvey Stuart E, Moraes Theo J, Teo Koon K, Mandhane Piush, Azad Meghan B, Simons Elinor, Pare Guillaume, Anand Sonia S (2024) Maternal smoking DNA methylation risk score associated with health outcomes in offspring of European and South Asian ancestry eLife 13:RP93260, https://doi.org/10.7554/eLife.93260.3</p> <p>1. CHILD_450K_R1_March2024_mateversmk_Regression_CpGWide.csv</p> <p>Maternal smoking using ever definition in CHILD (HM450K array).</p> <p>2. CHILD_450K_R1_March2024_matsmoke_Regression_CpGWide.csv</p> <p>Maternal smoking using current smoking definition in CHILD (HM450K array).</p> <p>3. CHILD_450K_R1_March2024_mblsmkexp_Regression_CpG_Wide.csv</p> <p>Maternal smoking exposure (hours per week) in CHILD (HM450K array).</p> <p>4. FAMILY_EPIC_R1_March2024_mateversmk_Regression_CpGWide.csv</p> <p>Maternal smoking using ever definition in FAMILY (customized EPIC array).</p> <p>5. FAMILY_EPIC_R1_March2024_matsmoke_Regression_CpGWide.csv</p> <p>Maternal smoking using current smoking definition in FAMILY (customized EPIC array).</p> <p>6. FAMILY_EPIC_R1_March2024_mblsmkexp_Regression_CpG_Wide.csv</p> <p>Maternal smoking exposure (hours per week) in FAMILY (customized EPIC array).</p> <p>7. START_450K_R1_March2024_mblsmkexp_Regression_CpG_Wide.csv</p> <p>Maternal smoking exposure (hours per week) in START (HM450K array).</p> <p>8. mateversmk_meta_annot_R1.csv</p> <p>Meta-analyzed european EWAS of maternal smoking using ever definition.</p> <p>9. matsmoke_meta_annot_R1.csv</p> <p>Meta-analyzed european EWAS of maternal smoking using current smoking definition.</p> <p>10. mblsmkexp_meta_annot_R1.csv</p> <p>Meta-analyzed european EWAS of maternal smoking exposure (hours per week).</p>
Epigenome-wide DNA Methylation and Pesticide Use in the Agricultural Lung Health Study
<p>An epigenome-wide association study of blood DNA methylation and pesticide use was conducted in adults in the Agricultural Lung Health Study. Sixteen specific pesticides were analyzed: dicamba, picloram, mesotrione, acetochlor, metolachlor, glyphosate, 2,4-Dichlorophenoxyacetic acid (2,4-D), atrazine, malathion, aldrin, chlordane, DDT, dieldrin, heptachlor, lindane, and toxaphene. 162 differentially methylated CpGs across 9 specific pesticides (acetochlor, atrazine, dicamba, glyphosate, malathion, metolachlor, mesotrione, picloram, and heptachlor.</p>
Simultaneous profiling of histone modifications and DNA methylation via nanopore sequencing
<p>Datasets that contain a minimum of nanopore reads sufficient for hidden Markov model training and for evaluating the performance of our computational tool - nanoHiMe at simultaneously calling CpG and/or adenine methylation on individual nanopore reads.<em> Ecoli</em>_PCR_amplicons_100k.tgz, <em>Ecoli</em>_PCR_MSssI_100k.tar.gz and <em>Ecoli</em>_PCR_pA-Hia5_100k.tar.gz are used for training new parameters of the emission distributions of individual <em>k</em>-mers from DNA template without modification, with fully methylated CpGs, and with partially methylated adenines, respectively. nanoHiMe_H3K27me3.fast5.tgz are the nanopore sequencing reads from H3K27me3 nanoHiMe-seq experiments in GM12878 cells and used for evaluating the performance of nanoHiMe at jointly calling CpG and adenine methylation.</p>
Data from: Developmental stress does not induce genome-wide DNA methylation changes in wild great tit (Parus major) nestlings
<p class="MsoNormal"><span>The environment experienced during early life is a crucial factor in the life of many organisms. This early life environment has been shown to have profound effects on morphology, physiology and fitness. However, the molecular mechanisms that mediate these effects are largely unknown, even though this is essential for our understanding of the processes that induce phenotypic variation in natural populations. DNA methylation is an epigenetic mechanism that has been suggested to explain such environmentally induced phenotypic changes early in life. To investigate whether DNA methylation changes are associated with experimentally induced early developmental effects, we cross-fostered great tit (<em>Parus major</em>) nestlings and manipulated their brood sizes in a natural study population. We assessed experimental brood size effects on pre-fledging biometry and behaviour. We linked this to genome-wide DNA methylation levels of CpG sites in erythrocyte DNA, using 122 individuals and an improved epiGBS2 laboratory protocol. Brood enlargement caused developmental stress and negatively affected nestling condition, predominantly during the second half of the breeding season, when conditions are harsher. Brood enlargement, however, affected nestling DNA methylation in only one CpG site and only if hatch date was taken into account. In conclusion, this study shows that nutritional stress in enlarged broods does not associate with direct effects on genome-wide DNA methylation. Future studies should assess whether genome-wide DNA methylation variation may arise later in life as a consequence of phenotypic changes during early development.</span></p>
Associated Dataset for Genome-wide DNA methylation patterns in bumble bee (Bombus vosnesenskii) populations from spatial-environmental range extremes
<p>The dataset contains the final methylation call set (n=14,627,533), variant calling file for population genomics analyses, analysis codes/scripts, and other associated files related to the research (Constitutive and variable patterns of genome-wide DNA methylation in populations from spatial-environmental range extremes of the bumble bee <em>Bombus vosnesenskii)</em>. Raw WGBS reads generated in this study have been deposited and are currently available at the National Center for Biotechnology Information (NCBI) Sequence Read Archive (SRA) under NCBI BioProject PRJNA956115.</p>
DNA methylation differences between stick insect ecotypes
<p><span>Epigenetic mechanisms, such as DNA methylation, can influence gene regulation and affect phenotypic variation, raising the possibility that they contribute to ecological adaptation. To begin to address this issue requires high-resolution sequencing studies of natural populations to pinpoint epigenetic regions of potential ecological and evolutionary significance. However, such studies are still relatively uncommon, especially in insects, and are mainly restricted to a few model organisms. Here, we characterize patterns of DNA methylation for natural populations of </span><span><em>Timema</em> <em>cristinae</em></span> <span>adapted to two host plant species (</span><span>i.e., </span><span>ecotypes).</span> <span>By integrating results from sequencing of whole transcriptomes, genomes, and methylomes, we investigate whether environmental, host, and genetic differences of these stick insects are associated with methylation levels of cytosine nucleotides in CpG context. We report an overall genome-wide methylation level for </span><em><span>T. cristinae</span></em> <span>of ~14%, being enriched in gene bodies and impoverished in repetitive elements. Genome-wide DNA methylation variation was strongly positively correlated with genetic distance (relatedness) but also exhibited significant host-plant effects. Using methylome-environment association analysis, we pinpointed specific genomic regions that are differentially methylated between ecotypes, with these regions being enriched for genes with functions in membrane processes. The observed association between methylation variation with genetic relatedness and the ecologically-important variable of host plant suggest a potential role for epigenetic modification in </span><em><span>T. cristinae</span></em> <span>adaptation. To substantiate such adaptive significance, future studies could test if methylation has a heritable component and the extent to which it responds to experimental manipulation in field and laboratory studies</span><span>.</span></p>
DNA methylation-based age prediction and sex-specific epigenetic aging in a lizard with female-biased longevity
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Data from: A cost-effective blood DNA methylation-based age estimation method in domestic cats, Tsushima leopard cats (Prionailurus bengalensis euptilurus), and Panthera species, using targeted bisulfite sequencing and machine learning models
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DNA methylation differences between stick insect ecotypes
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Data from: Non-invasive age estimation based on fecal DNA using methylation-sensitive high-resolution melting for Indo-Pacific bottlenose dolphins
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Data from: Developmental behavioural plasticity and DNA methylation patterns in response to predation stress in Trinidadian guppies
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Data for fitness analyses used in: Environmentally-induced DNA methylation is inherited across generations in water fleas (Daphnia magna)
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Early developmental carry-over effects on exploratory behaviour and DNA methylation in wild great tits (Parus major)
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