Browse
Submit Data
Databases
API
Help

Metabolomics,Unknown,Transcriptomics,Genomics,Proteomics

Dataset Information

0 Views

0 Connections

0 Citations

0 Reanalyses

0 Downloads

Omics score: 0

Methylation arrays (MethylationEPIC) of peripheral whole blood samples from adults aged between 19-96 years

ABSTRACT: Epigenetic remodeling is central to understanding the molecular mechanisms that occur and drive human aging. In fact, DNA methylation clocks are capable of estimating biological age, which determines human lifespan and healthspan. Here, using Illumina's MethylationEPIC array technology, we have profiled the DNA methylation landscape of peripheral whole blood samples from adult individuals ranging in age from 19 to 96 years.

ORGANISM(S): Homo sapiens

SUBMITTER: Mario F. Fraga

PROVIDER: E-MTAB-13431 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

ACCESS DATA

Json Xml

Similar Datasets

Methylation arrays (MethylationEPIC) of longitudinal cord and peripheral blood from children aged 0, 5 and 10 years old

Project description:Extensive epigenetic remodelling takes place during early life; however, the alterations of DNA methylation that occur during this period have not been sufficiently addressed by longitudinal studies. We made use of the most recent Illumina MethylationEPIC Beadchip platform to characterize genome-wide DNA methylation changes observed through the first 10 years of life in longitudinal blood samples of 11 subjects. We described numerous changes during the first 5 years, while many less alterations in the following 5 year period. Interestingly, we identified a subset of persistent DNA methylation changes of considerable magnitude along the first 10 years of life. Moreover, our data demonstrated that genes that suffer simultaneous hyper- and hypomethylation are functionally different from genes that are exclusively hyper- or hypomethylated. Furthermore, our results evidence that enhancer-associated methylation changes are different for the gain or loss of methylation. throughout hematopoietic maturation by analyzing multiple hematopoietic cell types at different developmental stages. We identified a plethora of DNA methylation changes that occur during human hematopoietic differentiation. Interestingly, we observed that T lymphocytes display a substantial enhancement of de novo CpG hypermethylation as compared to other hematopoietic cell populations.

2019-01-07 | E-MTAB-7069 | biostudies-arrayexpress

Methylation arrays (MethylationEPIC) of cord and peripheral whole blood from children born to mothers with obesity or gestational diabetes, with longitudinal follow-up during their first year of life

Project description:Epidemiological studies have associated maternal metabolic conditions such as obesity and gestational diabetes with poor health outcomes in the offspring. Epigenetic mechanisms may help explain the intrauterine influence that mothers have on their offspring during pregnancy. Here, using Illumina's MethylationEPIC array technology, we have longitudinally profiled the blood methylomes of children born to mothers with obesity and obesity with gestational diabetes, and healthy controls, during the first year of life (measurements at 0, cord blood; 6 and 12 months, peripheral blood).

2023-03-01 | E-MTAB-12728 | biostudies-arrayexpress

Methylation arrays (MethylationEPIC) of peripheral blood of individuals with evolving dementia

Project description:DNA methylation changes have been associated to dementia and cognitive decline. However, it is not clear if these changes arise before or during the onset of the pathologies. Here, using Illumina's MethylationEPIC array technology, we have profiled the methylomes of elderly, cognitively healthy individuals which go on to develop dementia or stay cognitively healthy. The subjects are profiled at a pre-diagnosis stage and at 4 year follow-up post-diagnosis.

2022-04-25 | E-MTAB-10600 | biostudies-arrayexpress

Methylation arrays (MethylationEPIC) of whole blood of individuals with type 1 diabetes

Project description:Type 1 diabetes (1a) diabetes is an autoimmune disease in which both environmental and genetic factors play a role. Epigenetic marks such as DNA methylation, being molecular links between the environment and the genome function, are sound candidates for the explanation of part of the aetiology and mechanisms of the disease. Here, the methylomes of the whole blood of 18 adult individuals with T1D were profiled using Illumina's MethylationEPIC array technology.

2020-12-22 | E-MTAB-8694 | biostudies-arrayexpress

Methylation arrays (MethylationEPIC) reveal specific DNA hypermethylation of T cells during human hematopoietic differentiation

Project description:Epigenetic regulation plays an important role in cellular development and differentiation. A detailed map of the DNA methylation dynamics that occur during cell differentiation would contribute to decipher the molecular networks governing cell fate commitment. We used the most recent Illumina MethylationEPIC Beadchip platform to describe the genome-wide DNA methylation changes observed throughout hematopoietic maturation by analyzing multiple hematopoietic cell types at different developmental stages. We identified a plethora of DNA methylation changes that occur during human hematopoietic differentiation. Interestingly, we observed that T lymphocytes display a substantial enhancement of de novo CpG hypermethylation as compared to other hematopoietic cell populations.

2018-06-08 | E-MTAB-6315 | biostudies-arrayexpress

Human pituitary adenomas DNA methylation profiling

Project description:86 samples processed with the Illumina Infinium®MethylationEPIC BeadChip, featuring 850k CpGs.

2020-01-02 | E-MTAB-7762 | biostudies-arrayexpress

Remodelling of the DNA methylation landscape of leukemic cells upon FOSL2 KO or CD34 + cells upon CRISPR mediated t(4:11) rearrangement

Project description:In this dataset we used the most recent Illumina MethylationEPIC Beadchip platform to describe the genome-wide DNA methylation changes observed upon FOSL2 KO in SEM leukemia cells. In addition, we explored the DNA methylation effect of t(4:11) translocation in the context of CD34+ cells. These samples correspond to additional experimental work from the manuscript and are related to the ArrayExpress entry E-MTAB-8505.

2021-03-12 | E-MTAB-10090 | biostudies-arrayexpress

DNA methylation profiling of core glioma stem cells: a novel glioblastoma cell population with diminished antigen presentation

Project description:Using induced pluripotent stem cell (iPSC) technology, we reprogrammed GBM derived cells (GBM-DCs) into embryonic-like cells termed as induced core-GSCs (ic-GSCs). The aim of this experiment was to characterize the DNA methylation profile of ic-GSCs using the Infinium MethylationEPIC array BeadChip (850K, Illumina). For this experiment, we selected three biological replicates of GBM-DCs (GBM-DC1, GBM-DC2 and GBM-DC3) and three independent clonal lines of ic-GSCs (ic-GSC#1, ic-GSC#3 and ic-GSC#7).

2022-05-11 | E-MTAB-10978 | biostudies-arrayexpress

Longitudinal study of DNA methylation during the first 5 years of life

Project description:Background: Early life epigenetic programming influences adult health outcomes. Moreover, DNA methylation levels have been found to change more rapidly during the first years of life. Our aim was the identification and characterization of the CpG sites that are modified with time during the first years of life. We hypothesize that these DNA methylation changes would lead to the detection of genes that might be epigenetically modulated by environmental factors during early childhood and which, if disturbed, might contribute to susceptibility to diseases later in life. Methods: The study of the DNA methylation pattern of 485577 CpG sites was performed on 30 blood samples from 15 subjects, collected both at birth and at 5 years old, using IlluminaÂ® Infinium 450 k array. To identify differentially methylated CpG (dmCpG) sites, the methylation status of each probe was examined using linear models and the Empirical Bayes Moderated t test implemented in the limma package of R/Bioconductor. Surogate variable analysis was used to account for batch effects. Results: DNA methylation levels significantly changed from birth to 5 years of age in 6641 CpG sites. Of these, 36.79 % were hypermethylated and were associated with genes related mainly to developmental ontology terms, while 63.21 % were hypomethylated probes and associated with genes related to immune function. Conclusions: Our results suggest that DNA methylation alterations with age during the first years of life might play a significant role in development and the regulation of leukocyte-specific functions. This supports the idea that blood leukocytes experience genome remodeling related to their interaction with environmental factors, underlining the importance of environmental exposures during the first years of life and suggesting that new strategies should be take into consideration for disease prevention. Longitudinal study including 15 samples

2016-06-15 | E-GEOD-83334 | biostudies-arrayexpress

The Methylome Landscape of Infant B-cell precursor Acute Lymphoblastic Leukemia (iB-ALL)

Project description:B-cell acute lymphoblastic leukemia (B-ALL) is the most prevailing childhood cancer. As predicated by its prenatal origin, infant B-ALL (iB-ALL) show a silent mutational landscape irrespective of the MLL rearrangement/status, suggesting that other regulatory mechanisms might be impaired in the context of the disease. Here we used the most recent Illumina MethylationEPIC Beadchip platform to describe the genome-wide DNA methylation changes observed in a total of 69 de novo MLL-AF4+, MLL-AF9+ and non-rearranged MLL iB-ALL leukemias uniformly treated according to Interfant 99/06 protocol. Please note that samples X8 and X9 (pool of B cells and BCP) correspond to samples 200340580160_R08C01 and 200340580161_R07C01 from study E-MTAB-6315, respectively.

2021-03-12 | E-MTAB-8505 | biostudies-arrayexpress

OmicsDI is part of the ELIXIR infrastructure

OmicsDI is an Elixir interoperability service. Learn more ›

Tweets

OmicsDI Databases

PRIDE
PeptideAtlas
MassIVE
JPOST Repository
Physiome Model Repository

EGA
EVA
ENA
LINCS
PAXDB
Cell Collective

MetaboLights
Metabolomics Workbench
MetabolomeExpress
GNPS
BioModels
FAIRDOMHub

ArrayExpress
dbGaP
ExpressionAtlas
GEO
NODE

Information

Databases
Help
API
Contact us
Code on GitHub
Terms of use
Submit Data