The DOI matches its Crossref record; 64 of the 64 references it cites were found in a registry or an index.
Differential DNA methylation with age displays both common and dynamic features across human tissues that are influenced by CpG landscape
Open accessAbstract
Background: DNA methylation is an epigenetic modification that changes with age in human tissues, although the mechanisms and specificity of this process are still poorly understood. We compared CpG methylation changes with age across 283 human blood, brain, kidney, and skeletal muscle samples using methylation arrays to identify tissue-specific age effects. Results: We found age-associated CpGs (ageCGs) that are both tissue-specific and common across tissues. Tissue-specific ageCGs are frequently located outside CpG islands with decreased methylation, and common ageCGs show the opposite trend. AgeCGs are significantly associated with poorly expressed genes, but those with decreasing methylation are linked with higher tissue-specific expression levels compared with increasing methylation. Therefore, tissue-specific gene expression may protect against common age-dependent methylation. Distinguished from other tissues, skeletal muscle ageCGs are more associated with expression, enriched near genes related to myofiber contraction, and closer to muscle-specific CTCF binding sites. Kidney-specific ageCGs are more increasingly methylated compared to other tissues as measured by affiliation with kidney-specific expressed genes. Underlying chromatin features also mark common and tissue-specific age effects reflective of poised and active chromatin states, respectively. In contrast with decreasingly methylated ageCGs, increasingly methylated ageCGs are also generally further from CTCF binding sites and enriched within lamina associated domains. Conclusions: Our data identified common and tissue-specific DNA methylation changes with age that are reflective of CpG landscape and suggests both common and unique alterations within human tissues. Our findings also indicate that a simple epigenetic drift model is insufficient to explain all age-related changes in DNA methylation.
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Systematic and integrative analysis of large gene lists using DAVID bioinformatics resources
Huang, D. W., Sherman, B. T., & Lempicki, R. A. (2009) · Nature Protocols · doi:10.1038/nprot.2008.211 · Cited by 35,890
Ultrafast and memory-efficient alignment of short DNA sequences to the human genome.
Langmead, B., Trapnell, C., Pop, M., & Salzberg, S. L. (2009) · Genome Biology · doi:10.1186/gb-2009-10-3-r25 · Cited by 21,849
Trapnell, C., Williams, B. A., Pertea, G., Mortazavi, A., Kwan, G., van Baren, M. J., Salzberg, S. L., Wold, B. J., & Pachter, L. (2010) · Nature Biotechnology · doi:10.1038/nbt.1621 · Cited by 15,556
Huang, D. W., Sherman, B. T., & Lempicki, R. A. (2009) · Nucleic Acids Research · doi:10.1093/nar/gkn923 · Cited by 14,108
TopHat: discovering splice junctions with RNA-Seq
Trapnell, C., Pachter, L., & Salzberg, S. L. (2009) · Bioinformatics · doi:10.1093/bioinformatics/btp120 · Cited by 11,617
A framework for variation discovery and genotyping using next-generation DNA sequencing data
DePristo, M. A., Banks, E., Poplin, R., Garimella, K. V., Maguire, J. R., Hartl, C., Philippakis, A. A., del Angel, G., Rivas, M. A., Hanna, M., et al. (2011) · Nature Genetics · doi:10.1038/ng.806 · Cited by 11,456
Adjusting batch effects in microarray expression data using empirical Bayes methods
Johnson, W. E., Li, C., & Rabinovic, A. (2007) · Biostatistics · doi:10.1093/biostatistics/kxj037 · Cited by 8,658
Functions of DNA methylation: islands, start sites, gene bodies and beyond
Jones, P. A. (2012) · Nature Reviews Genetics · doi:10.1038/nrg3230 · Cited by 6,061
A bivalent chromatin structure marks key developmental genes in embryonic stem cells
Bernstein, B. E., Mikkelsen, T. S., Xie, X., Kamal, M., Huebert, D. J., Cuff, J., Fry, B., Meissner, A., Wernig, M., Plath, K., et al. (2006) · Cell · doi:10.1016/j.cell.2006.02.041 · Cited by 5,361
Histone H3K27ac separates active from poised enhancers and predicts developmental state
Creyghton, M. P., Cheng, A. W., Welstead, G. G., Kooistra, T., Carey, B. W., Steine, E. J., Hanna, J., Lodato, M. A., Frampton, G. M., Sharp, P. A., et al. (2010) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.1016071107 · Cited by 4,302
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Chromatin-state discovery and genome annotation with ChromHMM
Ernst, J., & Kellis, M. (2017) · Nature Protocols · doi:10.1038/nprot.2017.124 · Cited by 910
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Weidner, C. I., Lin, Q., Koch, C. M., Eisele, L., Beier, F., Ziegler, P., Bauerschlag, D. O., Jöckel, K.-H., Erbel, R., Mühleisen, T. W., et al. (2014) · Genome Biology · doi:10.1186/gb-2014-15-2-r24 · Cited by 837
Epigenetic clock analysis of diet, exercise, education, and lifestyle factors
Quach, A., Levine, M. E., Tanaka, T., Lu, A. T., Chen, B. H., Ferrucci, L., Ritz, B., Bandinelli, S., Neuhouser, M. L., Beasley, J. M., et al. (2017) · Aging · doi:10.18632/aging.101168 · Cited by 801
Self-citationAn epigenetic clock analysis of race/ethnicity, sex, and coronary heart disease
Horvath, S., Gurven, M., Levine, M. E., Trumble, B. C., Kaplan, H., Allayee, H., Ritz, B. R., Chen, B., Lu, A. T., Rickabaugh, T. M., et al. (2016) · Genome Biology · doi:10.1186/s13059-016-1030-0 · Cited by 733
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Metadata
- Authors
- Day, K., Waite, L. L., Thalacker-Mercer, A., West, A., Bamman, M. M., Brooks, J. D., Myers, R. M., & Absher, D.
- Venue
- Genome Biology
- Published
- 2013-09-13
- Volume
- 14
- Issue
- 9
- Pages
- R102-R102
- ISSN
- 1465-6906 (ISSN-L)
- 1474-760X
- OpenAlex
- W1986462247
- Cited by
- 364
- References
- 64
- FWCI
- 11.03
- Type
- journal article
- Language
- en
- Open access
- gold
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