Aging effects on DNA methylation modules in human brain and blood tissue
By Steve Horvath, Yafeng Zhang, Peter Langfelder, René S. Kahn, M. Boks, K. van Eijk, L. H. van den Berg, Roel A. Ophoff. Genome Biology, 2012
625 citations recorded by the source corpus.
BackgroundSeveral recent studies reported aging effects on DNA methylation levels of individual CpG dinucleotides. But it is not yet known whether aging-related consensus modules, in the form of clusters of correlated CpG markers, can be found that are present in multiple human tissues. Such a module could facilitate the understanding of aging effects on multiple tissues.ResultsWe therefore employed weighted correlation network analysis of 2,442 Illumina DNA methylation arrays from brain and blood tissues, which enabled the identification of an age-related co-methylation module. Module preservation analysis confirmed that this module can also be found in diverse independent data sets. Biological evaluation showed that module membership is associated with Polycomb group target occupancy counts, CpG island status and autosomal chromosome location. Functional enrichment analysis revealed that the aging-related consensus module comprises genes that are involved in nervous system development, neuron differentiation and neurogenesis, and that it contains promoter CpGs of genes known to be down-regulated in early Alzheimer's disease. A comparison with a standard, non-module based meta-analysis revealed that selecting CpGs based on module membership leads to significantly increased gene ontology enrichment, thus demonstrating that studying aging effects via consensus network analysis enhances the biological insights gained.ConclusionsOverall, our analysis revealed a robustly defined age-related co-methylation module that is present in multiple human tissues, including blood and brain. We conclude that blood is a promising surrogate for brain tissue when studying the effects of age on DNA methylation profiles.
Subfields: Molecular Biology
View Paper: https://www.semanticscholar.org/paper/79bd78ac1c93470848aa50f49f86196c0ec4cb80
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Citations in the corpus
- An epigenetic biomarker of aging for lifespan and healthspan (2018, result): “Overall, we found that CpGs that had larger increases with aging tended to be located in CpG islands and enriched with polycomb group protein targets, consistent with what has been reported in previous epigenome wide studies of aging effects [4-7, 42].”
- Accounting for cellular heterogeneity is critical in epigenome-wide association studies (2014, methodology): “papers reporting age-related changes of DNAm profiles have been published [6-14].”
- Epigenetic clock analysis of diet, exercise, education, and lifestyle factors (2017, positive): “Chronological age has been shown to have a profound effect on DNA methylation levels [26-34].”
- Aging of blood can be tracked by DNA methylation changes at just three CpG sites (2014, methodology): “Age association of these CpGs was then tested using datasets GSE49904 [28], GSE41037 [29], GSE37008 [30], GSE34869 [34], and GSE20242 [15], and E-MTAB-487 [42].”
- DNA methylation age of human tissues and cell types (2013, methodology): “A vast literature characterizes genes or genomic regions that either get hypermethylated or hypomethylated with age [6-14].”
- An epigenetic clock analysis of race/ethnicity, sex, and coronary heart disease (2016, background): “Measures incorporating DNA methylation levels have recently given rise to a new class of biomarkers that appear informative of aging given that age has a profound effect on DNA methylation levels in most human tissues and cell types [10–18].”
- DNA methylation and healthy human aging (2015, methodology): “it has been repeatedly shown that DNA methylation levels at specific sites in the genome are so highly associated with age and that in some cases, they have been used to accurately predict chronological age (Bocklandt et al., 2011; Horvath et al., 2012; Hannum et al., 2013; Florath et al., 2014; Wei”