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8,338 results for “methylation”
Data from: Methylation of avpr1a in the cortex of wild prairie voles: effects of CpG position and polymorphism
DNA methylation can cause stable changes in neuronal gene expression, but we know little about its role in individual differences in the wild. In this study, we focus on the vasopressin 1a receptor (avpr1a), a gene extensively implicated in vertebrate social behaviour, and explore natural variation in DNA methylation, genetic polymorphism and neuronal gene expression among 30 wild prairie voles (Microtus ochrogaster). Examination of CpG density across 8 kb of the locus revealed two distinct CpG islands overlapping promoter and first exon, characterized by few CpG polymorphisms. We used a targeted bisulfite sequencing approach to measure DNA methylation across approximately 3 kb of avpr1a in the retrosplenial cortex, a brain region implicated in male space use and sexual fidelity. We find dramatic variation in methylation across the avrp1a locus, with pronounced diversity near the exon–intron boundary and in a genetically variable putative enhancer within the intron. Among our wild voles, differences in cortical avpr1a expression correlate with DNA methylation in this putative enhancer, but not with the methylation status of the promoter. We also find an unusually high number of polymorphic CpG sites (polyCpGs) in this focal enhancer. One polyCpG within this enhancer (polyCpG 2170) may drive variation in expression either by disrupting transcription factor binding motifs or by changing local DNA methylation and chromatin silencing. Our results contradict some assumptions made within behavioural epigenetics, but are remarkably concordant with genome-wide studies of gene regulation.
Topological Overlap Matrices for DNA Methylation data of Gestational Diabetes Cohort with BMI and Exposure Status
<p>DNA methylation in placenta was measured with the Infinium HumanMethylation450 BeadChip (Illumina, Inc) microarray, in a sample of 28 women, 20 of whom had a gestational diabetes (GD)-affected pregnancy and 8 who did not. We used GD status as our exposure variable, assuming that this has widespread effects on DNA methylation and on its correlation patterns. Our response, Y, is the standardized body mass index (BMI) in the offspring at the age of 5. For the 10,000 most variable probes, we provide 3 topological overlap matrices (TOM), which are used in our analysis (note that each of the following TOM matrices are a 10,000 by 10,000 symmetric matrix with row names and column names corresponding to the CpG probe IDs:</p> <ol> <li>TOM_Methylation_All_10k.rds: based on all 28 subjects, </li> <li>TOM_Methylation_E0_10k.rds: based on the 8 subjects without a GD-affected pregnancy</li> <li>TOM_Methylation_E1_10k.rds: based on the 20 subjects with a GD-affected pregnancy </li> </ol> <p>The BMI (phenotype) and GD status (exposure) are given in the following dataset:</p> <ol> <li>BMI_and_Exposure_Status.rds: 28 x 2 matrix of the phenotype and exposure. each row is a subject.</li> </ol> <p>Using our ECLUST method (preprint available at http://sahirbhatnagar.com/slides/manuscript1_SB_v4.pdf), we derive 77 clusters, and here we provide the 1st principal component of each cluster:</p> <ol> <li>Cluster_Summary_1stPC.rds: 28 x 77 matrix, where each row is a subject, in the same order as the BMI_and_Exposure_Status.rds data</li> <li>Cluster_CpGs_names.rds: a list of length 77, where each element of the list contains the list of CpG probe IDs contained in each of the clusters</li> </ol> <p>To read in the data use the readRDS function, e.g.:</p> <p>TOM_All <- readRDS(file = "TOM_Methylation_All_10k.rds")</p>
Supplementary information associated with a "Whole-Organism Integrated DNA Methylation and Transcriptomics Analysis of Butterfly Metamorphosis".
<p>Supplementary information, annotation and code related to the manuscript studying <em>Bicyclus anynana</em> development entitled "Whole-Organism Integrated DNA Methylation and Transcriptomics Analysis of Butterfly Metamorphosis".</p>
Genetic differentiation at methylation array probe SNPs leads to spurious results in meQTL discovery
<p>Data Associated with Figures 1 and 2 in Communications Biology Matters Arising: Genetic differentiation at methylation array probe SNPs leads to spurious results in meQTL discovery. Original data arising from B. Li et al. <i>Communications Biology</i> <a href="https://doi.org/10.1038/s42003-022-03353-5">https://doi.org/10.1038/s42003-022-03353-5</a> (2022)</p>
Differential methylation analysis in neuropathological confirmed dementia with Lewy bodies
<p>Authors: Paolo Reho, Sara Saez-Atienzar, Sultana Solaiman, Zalak Shah, Ruth Chia, Karri Kaivola, Bryan J. Traynor, Bension S. Tilley, Steve M. Gentleman, Angela K. Hodges, Dag Aarsland, Edwin S. Monuki, Kathy L. Newell, Randy Woltjer, Marilyn S. Albert, Ted M. Dawson, Liana S. Rosenthal, Juan C. Troncoso, Olga Pletnikova, Geidy E. Serrano, Thomas G. Beach, Hariharan P. Easwaran, Sonja W. Scholz</p> <p>Epigenome-wide association study investigating epigenetic modulations in a cohort of neuropathologically confirmed dementia with Lewy bodies cases and neurologically healthy controls.</p>
Reciprocal expression of MADS-box genes and DNA methylation reconfiguration initiate bisexual cones in spruce
<p>The naturally occurring bisexual cone of gymnosperms is considered to have been a potential intermediate stage in the origin of flowers, but the mechanisms governing bisexual cone formation remain largely elusive. Here, we employed transcriptomic and DNA methylomic analyses, together with hormone measurement, to investigate the molecular mechanisms underlying bisexual cone development in a conifer species <em>Picea crassifolia</em>. Our study reveals a "bisexual" expression profile in bisexual cones, especially in expression patterns of B-, C-class and <em>LEAFY</em> genes, supporting the out of male model. <em>GGM7</em> could be essential for initiating bisexual cones. DNA methylation reconfiguration in bisexual cones affects the expression of genes crucial for cone development, including <em>PcDAL12</em>, <em>PcDAL10</em>, <em>PcNEEDLY</em> and <em>PcHDG5</em>. Auxin likely plays an important role in the development of female structures of bisexual cones. This study unveils the potential mechanisms responsible for bisexual cone formation in conifers and may shed light on the development of bisexuality.</p>
Data from: Avian offspring prenatal DNA methylation response to maternal corticosterone dosing during reproduction
<p>Avian embryos develop in an egg composition which reflects both maternal condition and the recent environment of their mother. In birds, yolk corticosterone (CORT) influences development by impacting pre- and postnatal growth, as well as nestling stress responses and development. One possible mechanism through which maternal CORT may affect offspring development is via changes to offspring DNA methylation. We sought to investigate this, for the first time in birds, by quantifying the impact of manipulations to maternal CORT on offspring DNA methylation. We non-invasively manipulated plasma CORT concentrations of egg-laying female zebra finches (<em>Taeniopygia </em><em>castanotis</em>) with an acute dose of CORT administered around the time of ovulation and collected their eggs. We then assessed DNA methylation in the resulting embryonic tissue and in their associated vitelline membrane blood vessels, during early development (5 days after lay), using two established methods - liquid chromatography–mass spectrometry (LC-MS) and methylation-sensitive amplification fragment length polymorphism (MS-AFLP). LC-MS analysis showed that global DNA methylation was lower in embryos from CORT-treated mothers, compared to control embryos. In contrast, blood vessel DNA from eggs from CORT-treated mothers showed global methylation increases, compared to control samples. There was a higher proportion of global DNA methylation in the embryonic DNA of second clutches, compared to first clutches. Locus-specific analyses using MS-AFLP did not reveal a treatment effect. Our results indicate that an acute elevation of maternal CORT around ovulation impacts DNA methylation patterns in their offspring. This could provide a mechanistic understanding of how a mother's experience can affect her offspring's phenotype.</p>
A genome catalogue of mercury-methylating bacteria and archaea from sediments of a boreal river facing human disturbances
<p>Methyl mercury is a toxic compound produced by anaerobic microbes that biomagnifies in aquatic food webs, impacting animal and human health. Genome-based explorations of Hg methylators remain limited, particularly in the context of river ecosystems. To fill this knowledge gap, we created a genome catalogue of putative Hg-methylating microorganisms (based on the presence of <em>hgcAB</em>) from the sediments of a river impacted by two run-of-river hydroelectric dams, logging, and a wildfire. By using genome-resolved metagenomics, we uncovered a unique and diverse assemblage of Hg methylators dominated by members of the metabolically versatile Bacteroidota and particularly enriched in butyrate fermentative microbes. By comparing diversity and abundance of Hg methylators between sites that were subjected to different disturbances, we found that ongoing disturbances, such as input of organic matter related to logging activities. were particularly favorable to the establishment of a Hg-methylating niche. Lastly, for a deeper understanding of the environmental factors shaping Hg methylator diversity, we juxtaposed the Hg-methylating genome catalogue with the wider microbial community. The results suggest that Hg methylators respond to environmental conditions similarly to overall microbial community, and therefore it is crucial to interpret the diversity and abundance of Hg methylators within their specific ecological context.</p>
Blood DNA Methylation Profiling Identifies Cathepsin Z Dysregulation in Pulmonary Arterial Hypertension
<p>Ulrich, A., Wu, Y., Draisma, H., Wharton, J., Swietlik, E. M., Cebola, I., Vasilaki, E., Balkhiyarova, Z., Jarvelin, M. R., Auvinen, J., Herzig, K. H., Coghlan, J. G., Lordan, J., Church, C., Howard, L. S., Pepke-Zaba, J., Toshner, M., Wort, S. J., Kiely, D. G., Condliffe, R., … Rhodes, C. J. (2024). <a href="https://pubmed.ncbi.nlm.nih.gov/38184627/"><strong>Blood DNA methylation profiling identifies cathepsin Z dysregulation in pulmonary arterial hypertension</strong></a>. <em>Nature communications</em>, <em>15</em>(1), 330. https://doi.org/10.1038/s41467-023-44683-0</p> <p>Maternal educational attainment (MEA) shapes offspring health through multiple potential pathways. Differential DNA methylation may provide a mechanistic understanding of these long-term associations. We aimed to quantify the associations of MEA with offspring DNA methylation levels at birth, in childhood and in adolescence. Using 37 studies from high-income countries, we performed meta-analysis of epigenome-wide association studies (EWAS) to quantify the associations of completed years of MEA at the time of pregnancy with offspring DNA methylation levels at birth (n = 9 881), in childhood (n = 2 017), and adolescence (n = 2 740), adjusting for relevant covariates. MEA was found to be associated with DNA methylation at 473 cytosine-phosphate-guanine sites at birth, one in childhood, and four in adolescence. We observed enrichment for findings from previous EWAS on maternal folate, vitamin-B12 concentrations, maternal smoking, and pre-pregnancy BMI. The associations were directionally consistent with MEA being inversely associated with behaviours including smoking and BMI. Our findings form a bridge between socio-economic factors and biology and highlight potential pathways underlying effects of maternal education. The results broaden our understanding of bio-social associations linked to differential DNA methylation in multiple early stages of life. The data generated also offers an important resource to help a more precise understanding of the social determinants of health.</p>
Supplementary material from: Optimizing the Conversion of Bio-Oil from Haematococcus pluvialis to Fatty Acid Methyl Esters
<p>Data obtained in the thermal characterization of bio-oil and biodiesel derived from Haematococcus pluvialis microalgae. This document contains the data used to generate the plots shown in Figure 3 of the paper.</p> <p>Content:</p> <ul> <li>Bio-oil FTIR results</li> <li>Biodiesel FTIR results</li> <li>Bio-oil TGA results</li> <li>Biodiesel TGA results</li> <li>Bio-oil DSC (pour point) results</li> <li>Biodiesel DSC (pour point) results</li> <li>Biodiesel DSC (heat of combustion) results</li> </ul>
Dataset for the publication: Non-oxidative conversion of methanol to dimethyl ether, methyl formate and dimethoxymethane over Cu/Hβ catalyst: Tailoring product selectivity
<p>The dataset covers the research data of the publication in ChemCatChem with the title "Non-oxidative conversion of methanol to dimethyl ether, methyl formate and dimethoxymethane over Cu/Hβ catalyst: Tailoring product selectivity" (DOI: <a href="https://doi.org/10.1002/cctc.202301704">10.1002/cctc.202301704</a>). The provided data comprehend the main experimental data obtained in the study including material characterisation (XRD, CO-DRIFTS, H2-TPD, pyridine IR, N2 physisorption) and the catalytic data of the investigated Cu-loaded zeolites in a continuous gas-phase fixed-bed reactor.</p>
Promoter methylation leads to Hepatocyte Nuclear Factor 4A loss and pancreatic cancer aggressiveness.
<p><i>Efforts to decode pancreatic ductal adenocarcinoma (PDAC) heterogeneity and the consequent therapeutic selection remains a challenge. We aimed to characterize epigenetically regulated pathways involved in PDAC progression.</i></p><p><i>Global DNA methylation analysis in pancreatic cancer patient tissues and cell lines was performed to identify differentially methylated genes. Targeted bisulfite sequencing and in vitro methylation reporter assays were employed to investigate the direct link between sitespecific methylation and transcriptional regulation. A series of in vitro loss- and gain-of function studies, and in vivo xenograft and the KPC (LSL-KrasG12D/+; LSL-Trp53R172H/+; Pdx1-Cre) mouse models were used to assess pancreatic cancer cell properties. Gene and protein expression analyses were performed in three different cohorts of pancreatic cancer patients and correlated to clinicopathological parameters.</i></p><p><i>We identify Hepatocyte Nuclear Factor 4A (HNF4A) as a novel target of hypermethylation in pancreatic cancer and demonstrate that site-specific proximal promoter methylation drives HNF4A transcriptional repression. Expression analyses in patients, indicate the methylation-associated suppression of HNF4A expression in pancreatic cancer tissues. In vitro and in vivo studies reveal that HNF4A is a novel tumor suppressor in pancreatic cancer, regulating cancer growth and aggressiveness. As evidenced in both the KPC mouse model and human pancreatic cancer tissues, HNF4A expression declines significantly in the early stages of the disease. Most importantly, HNF4 loss correlates with poor overall patient survival.</i></p>
Supplemental data for the publication: "Algal methylated compounds shorten the lag phase of Phaeobacter inhibens bacteria"
<p><strong>Data S1: </strong><em>P. inhibens </em>feature table (genes) with results from co-cultivation RNA-sequencing run. The dataset includes bacterial gene accession numbers, functional annotations, transcript abundances (TPM normalized read counts) and results of DESeq2 differential gene expression analysis. (Fig. 1, figs. S1A, S3-S4, tables S1-S2). </p> <p><strong>Data S2: </strong><em>P. inhibens </em>feature table (genes) with results from lag phase RNA-sequencing run. The dataset includes bacterial gene accession numbers, functional annotations, transcript abundances (TPM normalized read counts) and results of DESeq2 differential gene expression analysis. (Fig. 4A, figs. S10-S13, tables S6-S7). </p> <p><strong>Data S3: </strong><em>Emiliania huxleyi </em>CCMP3266 sGenome gene annotation file version 2 (GFF3 format).</p> <p><strong>Data S4: </strong>Feature quantification obtained using Compound Discoverer. The table presents the output analysis using Compound Discoverer (v3.3) with a putative identification of metabolites. Each identified metabolite (each row) contains a sub-table under the + tab (on the left side) that specifies the feature quantification in each analyzed sample. Sample ID appears in the “Study File ID” column in each sub-table. Values in the columns “Exchange Rate [%]: 0” and “Exchange Rate [%]: 1” represent relative abundances of the molecules in their unlabeled form and with single <sup>13</sup>C-label (M+1 isotopologue), respectively. Compounds marked with “1” in the “Tags” column were further validated using standards. The average M+1 isotope abundance [%] for <em>S</em>-Adenosylmethionine (SAM) and 5'-<em>S</em>-Methyl-5'-thioadenosine (MTA)—as reported in Fig. 4D—were calculated by averaging the values in the “Exchange Rate [%]: 1” column from samples supplemented with <sup>13</sup>C-labeled and unlabeled DMSP, respectively. Detailed information regarding the isotope abundances of SAM and MTA can be found in the tab “SAM and MTA isotope abundance” in the table. In the tab “Features Positive Mode” samples F10, F12, F14, and F16 were supplemented with <sup>13</sup>C-labeled DMSP and samples F2, F4, F6, F8 were supplemented with unlabeled DMSP. In the tab “Features Negative Mode” samples F2, F3, F4, and F5 were supplemented with <sup>13</sup>C-labeled DMSP and samples F10, F11, F12, F13 were supplemented with unlabeled DMSP.</p>
BAF155 Methylation Drives Metastasis By Hijacking Super-enhancers and Subverting Anti-tumor Immunity
<p>Subunits of the chromatin remodeler SWI/SNF are the most frequently disrupted genes in cancer. However, how post-translational modifications (PTM) of SWI/SNF subunits elicit epigenetic dysfunction remains unknown. Arginine-methylation of BAF155 by coactivator-associated arginine methyltransferase 1 (CARM1) promotes triple negative breast cancer (TNBC) metastasis. Herein, we discovered the dual roles of methylated-BAF155 (me-BAF155) in promoting tumor metastasis: activation of super-enhanceraddicted oncogenes by recruiting BRD4, and repression of interferon / pathway genes to suppress host immune response. Pharmacological inhibition of CARM1 and BAF155 methylation not only abrogated the expression of an array of oncogenes, but also boosted host immune responses by enhancing the activity and tumor infiltration of cytotoxic T cells. Moreover, strong me-BAF155 staining was detected in circulating tumor cells from metastatic cancer patients. Despite low cytotoxicity, CARM1 inhibitors strongly inhibited TNBC cell migration in vitro, and growth and metastasis in vivo. These findings illustrate a unique mechanism of arginine methylation of a SWI/SNF subunit that drives epigenetic dysregulation, and establishes me-BAF155 as a therapeutic target to enhance immunotherapy efficacy.</p>
Methylation and gene expression data from: Differential DNA methylation across environments has no effect on gene expression in the eastern oyster
<p>1. It has been hypothesized that environmentally induced changes to gene body methylation could facilitate adaptive transgenerational responses to changing environments.</p> <p>2. We compared patterns of global gene expression (Tag-seq) and gene body methylation (reduced representation bisulfite sequencing) in 80 eastern oysters (<i>Crassostrea virginica</i>) from six full-sib families, common gardened for 14 months at two sites in the northern Gulf of Mexico that differed in mean salinity.</p> <p>3. At the time of sampling, oysters from the two sites differed in mass by 60% and in parasite loads by nearly two orders of magnitude. They also differentially expressed 35% of measured transcripts. However, we observed differential methylation at only 1.4% of potentially methylated loci in comparisons between individuals from these different environments, and little correspondence between differential methylation and differential gene expression.</p> <p>4. Instead, methylation patterns were largely driven by genetic differences among families, with a PERMANOVA analysis indicating nearly a two orders of magnitude greater number of genes differentially methylated between families than between environments.</p> <p>5. An analysis of CpG observed/expected values (CpG O/E ) across the <i>C. virginica</i> genome showed a distinct bimodal distribution, with genes from the first cluster showing the lower CpG O/E values, greater methylation, and higher, and more stable gene expression, while genes from the second cluster showed lower methylation, and lower and more variable gene expression.</p> <p>6. Taken together, the differential methylation results suggest that only a small portion of the <i>C. virginica</i> genome is affected by environmentally induced changes in methylation. At this point, there is little evidence to suggest that environmentally induced methylation states would play a leading role in regulating gene expression responses to new environments.</p>
Mutation and methylation data for study: Assessment of the molecular heterogeneity of E-cadherin expression in invasive lobular breast cancer
<p>Processed mutation data and DNA methylation beta values published with this study.</p>
Supplementary materials for "Integration of public DNA methylation and expression networks via eQTMs improves prediction of functional gene–gene associations"
<p>This repository contained supplementary materials in the study named: "<strong>Integration of public DNA methylation and expression networks via eQTMs improves prediction of functional gene–gene associations.</strong>"</p> <p>For extracting all files from the downloaded tar.gz file, the following commanda could be used:</p> <pre><code>tar -xf supplementary_materials.tar.gz </code></pre> <p>The supplementary_materials/data diretcory contains the following sections:</p> <p></p> <ol> <li>eqtm_predictions: This contains the training and testing datasets for the eQTM prediction procedures</li> <li>public_methylation_data_and_pca: This contains the harmonized public DNA methylation dataset and its first 100 PCA components</li> <li>cca_data: This contains the CCA components for the public DNA methylation and gene expression datasets for the negative eQTMs, and the input datasets for the functional gene pair prediction analylsis.</li> <li>gene_enrichment_results_for_cca: This contains the gene enrichment results for the CCA components for negative and positive eQTMs</li> </ol> <p>The supplementary_materials/model directory contains the following sections:</p> <ol> <li>disease_tissue_predictions: This contains the models trained for tissue prediction and disease prediction based on the PCA components from the public DNA methylation data</li> <li>eqtm_predictions: This contains models trained for eQTM prediction</li> <li>cca_transformations: This contains CCA transformation models and models for STRING gene pair predictions </li> </ol>
Data from: Inheritance of DNA methylation differences in the mangrove Rhizophora mangle
<p>This record contains supplementary information for the article "Inheritance of DNA methylation differences in the mangrove Rhizophora mangle" published in Evolution&Development. It contains the barcodes (barcodes.txt), the reference contigs (contigs.fasta.gz), the annotation of the reference contigs (mergedAnnot.csv.gz), the SNPs (snps.vcf.gz), the methylation data (methylation.txt.gz), and the experimental design (design.txt). All data are unfiltered. Short reads are available on SRA (PRJNA746695). Note that demultiplexing of the pooled reads (SRX11452376) will fail because the barcodes are already removed and the header information is lost during SRA submission. Instead, use the pre-demultiplexed reads that are as well linked to PRJNA746695.</p> <p><br> </p> <p><strong>Table S13 (TableS13_DSSwithGeneAnnotation.offspringFams.csv.gz): </strong></p> <p>Differential cytosine methylation between families using the mother data set. The first three columns fragment number ("chr"), the position within the fragment ("pos"), and the sequence context ("context"). Columns with the pattern FDR_<X>_vs_<Y> contain false discovery rates of a test comparing population X with population Y. Average DNA methylation levels for each population are given in the columns "AC", "FD", "HI", "UTB", "WB", and "WI". The remaining columns contain the annotation of the fragment, for example whether it matches to a gene and if yes, the gene name ID and description are provided.</p>
Supporting data and code for: Thiophanate-methyl and carbendazim resistance in Fusicoccum amygdali, the causal agent of constriction canker of peach and almond
<p>This is the first release of the final data and code for the article accepted for publication in Plant Pathology journal. It contains all the necessary scripts to perform the data analysis and to produce the Figures. All the necessary data can be found in the 'data' folder.</p>
Maternal Mediterranean diet in pregnancy and newborn DNA methylation: a meta-analysis in the PACE Consortium
<p>Higher adherence to the Mediterranean diet during pregnancy may be related to offspring cord blood DNA methylation. In a meta-analysis of epigenome-wide association studies (EWAS) in 2802 mother-child pairs from 5 cohorts we calculated the relative Mediterranean diet (rMED) score and an adjusted rMED excluding alcohol (rMEDp). rMEDp was associated with cord blood DNA methylation at cg23757341.</p>
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Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.