Background: The well-established association of chronological age with changes in DNA methylation is primarily founded on the analysis of large sets of blood samples, while conclusions regarding tissue-specificity are typically based on small number of samples, tissues and CpGs. Here, we systematically investigate the tissue-specific character of age-related DNA methylation changes at the level of the CpG, functional genomic region and nearest gene in a large dataset. Results: We assembled a compendium of public data, encompassing genome-wide DNA methylation data (Illumina 450k array) on 8092 samples from 16 different tissues, including 7 tissues with moderate to high sample numbers (Dataset size range 96–1202, N = 2858). In the 7 tissues (brain, buccal, liver, kidney, subcutaneous fat, mono- total cytes and T-helper cells), we identified 7850 differentially methylated positions that gained (gain-aDMPs; cut-offs: P ≤ 0.05, effect size ≥ 2%/10 years) and 4,287 that lost DNA methylation with age (loss-aDMPs), 92% of which had bonf not previously been reported for whole blood. The majority of all aDMPs identified occurred in one tissue only (gain- aDMPs: 85.2%; loss-aDMPs: 97.4%), an effect independent of statistical power. This striking tissue-specificity extended to both the functional genomic regions (defined by chromatin state segmentation) and the nearest gene. However, aDMPs did accumulate in regions with the same functional annotation across tissues, namely polycomb-repressed CpG islands for gain-aDMPs and regions marked by active histone modifications for loss-aDMPs. Conclusion: Our analysis shows that age-related DNA methylation changes are highly tissue-specific. These results may guide the development of improved tissue-specific markers of chronological and, perhaps, biological age. Keywords: DNA methylation, 450 k, Tissue-specific, Ageing Background another species, namely the mouse . The strength of the associations has led to the development of multiple The association between DNA methylation and age in predictors that can accurately estimate chronological humans is well established for whole blood [1–10], and age from methylation levels at a limited set of CpG sites also in adipose tissue, brain and mesenchymal stem cells, [18–20]. While most predictors are trained on whole- loci have been found where DNA methylation changes blood DNA methylation data, one age predictor works with age [11–13]. A prime example is the CpGs near the independent of tissue type [18–20]. Intuitively, the high ELOVL2 gene that exhibit consistent age-related changes precision of the tissue-independent age predictor may in blood, hMSCs  and teeth  and other tissues rely on combining the cumulative information of CpGs [15, 16], an association that even extends to tissue from whose DNA methylation level changes with age in mul tiple tissues simultaneously . However, current views *Correspondence: email@example.com of the extent of tissue-specificity versus tissue-shared Molecular Epidemiology, Department of Biomedical Data Sciences, age-related DNA methylation are based on relatively Leiden University Medical Center, Einthovenweg 20, 2333 ZC Leiden, The Netherlands small-scale studies with repect to the number of samples, Full list of author information is available at the end of the article © The Author(s) 2018. This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creat iveco mmons .org/licen ses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creat iveco mmons .org/ publi cdoma in/zero/1.0/) applies to the data made available in this article, unless otherwise stated. Slieker et al. Epigenetics & Chromatin (2018) 11:25 Page 2 of 11 Table 1 Overview of studies of age-related DNA methylation changes in multiple tissues Species Tissues (n) CpGs (Platform) Comparison of overlap at each level Ref CpG Functional Gene genomic region Humans Human Buccal (96), liver (147), kidney (171) 428,279 + + + Current study Th cells (214), brain (603), SC fat (648), monocytes (1202) (Illumina 450 k) Human Cervix (3), bladder (5), intestine (5), kidney (6), head/neck 1413 – – –  (11), brain (12), pleura (18), placenta (19), lung (49), blood (GoldenGate) (85) Human Muscle (51), blood (71), brain (78), kidney (83) 26,486 + – – (GO terms)  (27 k array) Human Neuron (29), glia (29), MSCs (92), whole blood (656) 429,789 + – –  (Illumina 450 k) Rodents Rat Fat (3), liver (5–6) 40,000 – – –  (HELP assay) Mouse Liver (15), heart (15), lung (16), cortex (16) 1,230,000 + – – (GO terms)  (RRBS) tissues and/or CpG sites (Table 1). The three previous aDMP extended to multiple tissues. Gain of methylation human studies on tissue-specificity included between 4 was observed in blood (N = 3295, Fig. 1a) and extended and 92 samples per tissue (and 656 whole-blood samples) to all tissues investigated except cerebellum (Fig. 1b) in interrogating 1413, 26,486 and 429,789 CpG sites [13, 21, line with previous reports . 22]. Although two of these studies concluded that age- To systematically study the occurrence of tissue-spe- related DNA methylation are tissue-specific [13, 21], the cific and tissue-shared aDMPs, we identified aDMPs in third reported that age-related changes were both shared tissues for which a moderate to large sample size was across tissues and tissue-specific . However, small available (96 ≤ N≤1202; Additional file 1: Table S1), numbers of tissues, samples and CpGs are biased towards which included brain (N = 380), buccal (N = 96), liver finding tissue-specificity. Hence, conclusive evidence (N = 147), kidney (N = 171), subcutaneous fat (SAT, whether age-related changes are tissue-specific or tissue- N = 648), monocytes (N = 1202) and T-helper cells ( Th shared is lacking. cells, N = 214). As a comparison, whole-blood aDMPs Here, we report on a systematic genome-wide analysis were obtained from our previous work (N = 3295, ). of age-related DNA methylation changes in a collection We focused on a conservative set of aDMPs defined by of 2858 methylomes from 7 tissues and show that the genome-wide significance (P ≤ 0.05) and a robust age- bonf DNA methylation changes are highly tissue-specific and related gain or loss that was larger than 2% per 10 years. cannot be attributed to differences in statistical power. Out of the 428,279 CpGs investigated, 7850 unique CpGs This tissue-specificity is not restricted to the individual gained DNA methylation in one or more tissues (gain- CpG site but extends to the level of the functional region aDMPs) and 4287 unique CpGs lost DNA methylation and the nearest gene to which a CpG maps. However, in in one or more tissues (loss-aDMPs). The number of every tissue, it is the same functional region (non-CGI, aDMPs identified in each tissue varied strongly, with CGI, polycomb binding site, etc.) that accumulates age- the highest number in buccal (4857 aDMPs in N = 96; related changes albeit at distinct locations in the genome. Fig. 2a, Additional File 2: Table S2) and the lowest num- ber in Th cells (39 aDMPs in N = 214, Additional File 2: Results Table S2). As expected, whole-blood showed a substan- To investigate age-related DNA methylation changes tial overlap with monocytes (62 gain-aDMPs, 84 loss- between tissues, Illumina 450k DNA methylation data aDMPs, Additional File 2: Table S2) and Th cells (20 were obtained from public repositories for 16 tissues gain-aDMPs, 3 loss-aDMPs, Additional File 2: Table S2). encompassing in total 8092 individuals (Additional file 1: Therefore, whole blood was not included in subsequent Table S1). First, we revisited the age-related differentially comparative analyses. The number of gain- versus loss- methylated position (aDMP) near ELOVL2 (cg16867657), DMPs differed between tissues. For example, in liver, to test whether the tissue-independent character of this aDMPs mainly gained DNA methylation (gain 2499, loss Slieker et al. Epigenetics & Chromatin (2018) 11:25 Page 3 of 11 Fig. 1 Age-related change in DNA methylation in ELOVL2. a DNA methylation (y axis) against age (x axis) in blood for the ELOVL2 CpG (cg16867657). b DNA methylation (y axis) against age (x axis) in other tissues for the ELOVL2 CpG (cg16867657) Fig. 2 Identification of aDMPs. a Number of identified gain- and loss-aDMPs (y axis) in this study for each tissue (x axis). b Slopes of identified gain- and loss-aDMPs (y axis) for each tissue (x axis). c Overlap between tissues in identified gain- and loss-aDMPs. In the diagonal cells the number of aDMPs unique for that tissue, the upper number represents the percentage, the lower number the number of overlapping aDMPs. Blue— gain-aDMPs; Purple—loss-aDMPs 411; Fig. 2a, Additional File 2: Table S2), while in mono- The majority of aDMPs are tissue‑specific cytes aDMPs mainly lost DNA methylation (gain 83, loss The comparison of aDMPs between tissues showed that 574, Additional File 2: Table S2). Not only the number the large majority of aDMPs were tissue-specific (85.2% of DMPs but also the rates of change with age varied for gain-aDMPs and 97.4% for loss-aDMPs). Albeit low, between tissues (Fig. 2b). The differences in number per the number of aDMPs shared between multiple tissues was tissue was not explained by either the known replication higher for gain-aDMPs than for loss-aDMPs (Fig. 2c). Of rate of the stem cells of the tissues analyzed (r = − 0.05, the gain-aDMPs, 1161 (14.8%) were identified in ≥ 2 tissues P = 0.90; Additional file 3: Fig. S1A) or the number of (Fig. 3a). Only 2 gain-aDMPs were found in all 7 tissues individuals used in each tissue (r = − 0.47, P = 0.24; studied and both mapped to the ELOVL2 locus (Addi- Additional file 3: Fig. S1B). tional file 4: Table S3), thus underscoring that the strong Slieker et al. Epigenetics & Chromatin (2018) 11:25 Page 4 of 11 c d e g Fig. 3 Characterization of gain-aDMPs. a Frequency of aDMPs (y axis) against the number of tissues the aDMPs was identified in (x axis). b Enrichment of gain-aDMPs in chromatin segmentations expressed as an odds ratio, grey non-significant. c Percentage (top) and odds ratios (bottom) of aDMPs in CGIs, shores and non-CGIs. Blue enriched, red depleted, grey non-significant. d Percentage (top) and odds ratios (bottom) of aDMPs in EZH2 binding sites (ChIP-seq, any cell type, ENCODE). Blue enriched, red depleted, grey non-significant. e Frequency of CpG islands (y axis) against the number of tissues a CpG island was identified in ( x axis). f Frequency of genes (y axis) against the number of tissues a gene was identified in (x axis). g Expression (y axis, RPKM) of genes near gain-aDMPs per tissue (x axis). Abbreviations: TssA active TSS, TssAFlnk flanking active TSS, TxFlnk transcr. at gene 5′ and 3′, Tx strong transcription, TxWk weak transcription, EnhG genic enhancers, Enh enhancers, ZNF/Rpts ZNF genes + repeats, Het heterochromatin, TssBiv bivalent/poised TSS, BivFlnk flanking bivalent TSS/Enh, EnhBiv bivalent enhancer, ReprPC repressed polycomb, ReprPCWk weak repressed polycomb, Quies quiescent/low tissue-shared association of these CpGs with age is excep- criterion. This approach resulted in 10,249 aDMPs tional. Loci consistently identified in blood [3 ] were also across the 7 tissues (brain 4195, buccal 4857, liver 1636, found in a subset of the tissues, that is 971 aDMPs (8.0%) kidney 499, monocytes 109, SAT 23), and these aDMPs overlapped with the 7477 CpGs previously identified in were equally tissue-specific between tissues (Additional blood including FHL2 (5 tissues) and PENK (4 tissues). file 5: Fig. S2B). In these equally sized datasets, we To exclude that the tissue-specificity was only due observed that the aDMPs identified in one tissue were to the differences in size of the datasets, we performed significant in other tissues at a less stringent P value three additional analyses. First, we identified aDMPs cut-off (P < 0.001), suggesting that there may be a weak based on an effect size criterion only, thus eliminating aDMP effect. However, this effect was much weaker the effect of statistical power. While CpGs in each of as compared to the tissue the aDMP was identified in the tissues have an equal chance to become an aDMP, (Additional file 5 : Fig. S2A–B, Additional file 6 : Fig. S3). tissue-specificity was again observed in each of the tis - Thirdly, the tissue-specificity of aDMPs was confirmed sues in the 37,136 aDMPs identified (brain 4786, buccal when we determined the age-related slope of the set of 27,884, liver 4225, kidney 3694, monocytes 769, SAT originally identified aDMPs in the all available 16 tissues 222, Th cells 721; Additional file 5: Fig. S2A). Secondly, (Additional file 6: Fig. S3). Together, these analyses rein- we identified aDMPs in equally sized datasets (N = 96, forced the interpretation that aDMPs are truly tissue-spe- based on smallest tissue datasets) with both a P value cific and not due to differences in statistical power to detect (P ≤ 0.05) and effect size cut-off (> 2%/10 years) aDMPs between tissues. Hence, age-related methylation bonf Slieker et al. Epigenetics & Chromatin (2018) 11:25 Page 5 of 11 changes occurring in one tissue are generally not indicative primarily occur at regions are both polycomb-repressed of age-related changes at the same CpGs in another tissue. and CGIs. Finally, when the same enrichment analy- Identified aDMPs showed little overlap with the 353 ses were performed on the whole-blood aDMPs, similar CpGs from Horvath’s age predictor  (Additional file 7: results were observed as compared to the other seven Fig. S4). The maximum overlap with gain-aDMPs was tissues (Additional file 10: Fig. S8A–C). found in brain (13 gain-aDMPs) and with loss-aDMPs in monocytes (6 loss-aDMPs). This is not unexpected, given CGIs and genes that gain methylation are that Horvath’s age predictor was trained using a penal- also tissue‑specific ized regression method aimed at identifying a sparse set Our analysis showed that, although individual gain- of independent predictors and the fact that it was based aDMPs are tissue-specific, their genomic annotation is on the Illumina 27k array (and hence did for example shared. This was not due to different CpGs in the same not include the ELOVL2 CpGs). When Horvath’s tissue- CGI being identified as aDMPs across different tissues. independent age predictor was applied to the compen- This would go against the interpretation that gain-aDMPs dium of 16 tissues, the correlation between chronological are mainly tissue-specific. However, this was not the case. age and predicted age was high although the precision of Of the 1,722 CGIs harbouring at least one gain-aDMP in the prediction for individual samples was often limited at least one tissue, 70.1% were unique (Fig. 3e). The tis - (Additional file 8: Fig. S5). Only a minority of the CpGs sue-specificity further extended towards genes: mapping that were included in Horvath’s age predictor show a gain-aDMPs to their nearest gene, resulting in 2029 genes strong association with age and the strongest tissue-spe- that were unique for a tissue (64.8%). Only one gene was cific aDMPs are missing (Additional file 9: Fig. S6). This found in all 7 tissues, namely (as expected) the ELOVL2 illustrates the potential value of tissue-specific analyses gene (Fig. 3f and Additional file 4: Table S3). The 12 genes to gain insight into the mechanisms linking age-related that were identified in 6 out of 7 tissues included BMI1 DNA methylation changes with tissue-specific ageing. (involved in the DNA damage response) and LIN28B (a microRNA that enhances IGF-2 translation). Counting the number of gain-aDMPs near a gene per tissue corrobo- Gain‑aDMPs are tissue‑specific but share their functional rated the tissue-specificity of genes. Genes with > 5 gain- annotation aDMPs in one tissue had few in other tissues (Additional Genomic annotation showed that gain-aDMPs were file 12: Table S4). Examples were PRRT1 in the brain (brain highly enriched at CpG islands (CGIs) and their shores 24, buccal 5, liver 7, kidney 1, monocytes 0, SAT 1, Th cell as compared with non-CGI sequences in each of the 2) and HOXD in buccal cells (buccal 26, other tissues 0). seven tissues (OR 1.6–15.6, P < 0.0001, Fig . 3c) and Next, we investigated the function of genes near gain- also in whole blood (OR 17.5, P < 0.0001, Additional aDMPs. In brain (84 processes), buccal (151 processes), file 10: Fig. S8A). These findings are in line with previ - liver (64 processes) and kidney (59 processes), multiple ous findings [1 , 16, 25]. Utilizing reference chromatin biological processes were enriched among nearest genes segmentation data (marking the biological function of (P ≤ 0.05). Commonly and strongly enriched processes bonf genomic regions) of primary tissues matching the tis- included embryonic morphogenesis (number of genes sues studied here , we found that gain-aDMPs pref- in brain 82, buccal 98, liver 69, kidney 37; P < 0.0001, bonf erentially occur at Bivalent Enhancers (OR 2.8–8.0, Additional file 13: Table S5) and regulation of transcrip- P < 0.0001, Fig . 3b) and Repressed Polycomb (3.4–9.8, tion (number of genes in brain 231, buccal 318, liver 209, P < 0.0001), both characterized by the polycomb repres- kidney 134; P < 0.0001, Additional file 13: Table S5). bonf sion mark H3K27me3. The latter observation was con - Finally, we investigated the expression of genes near firmed by the frequent co-occurrence of gain-aDMPs gain-aDMPs using public gene expression data on tissues with binding sites of polycomb (PcG) repressive com- matching those studied here (GTEX data, frontal cortex, plex 2 (PRC2) protein EZH2. At least one-third of the N = 108; oesophagus–mucosa, N = 286; liver, N = 119; identified gain-aDMPs overlapped with an EZH2 bind - kidney cortex, N = 32; whole blood, N = 393; age range ing site increasing to almost two-thirds for buccal cells 20–79 years). The baseline expression of genes was low (OR 12.7, P < 0.0001, Fig . 3d). To address whether the (in line with their repressed state and developmental enrichments for CGIs and EZH2 binding are independ- function), and we did not observe evidence for changes in ent or reflect the same underlying biology, we analysed gene expression (Fig. 3g and Additional file 11: Fig. S7B). both annotations together. Gain-aDMPs were twofold Furthermore, there was little overlap between these genes enriched (1.9–2.8, P < 0.0001) at genomic regions that and those previously reported to have a changed expres- were both CGIs and binding EZH2 as compared with sion with age in whole blood (brain 91, buccal 104, liver regions that featured only one of the annotations (Addi- 88, kidney 28, monocyte 1, SAT 3, Th cell 0) . tional file 11: Fig. S7A). This suggests that gain-aDMPs Slieker et al. Epigenetics & Chromatin (2018) 11:25 Page 6 of 11 Loss‑aDMPs are enriched for active regions in ≥ 2 tissues (Fig. 4b). Again, contrasting with gain- including tissue‑specific enhancers aDMPs, loss-aDMPs were particularly overrepresented In contrast to gain-aDMPs, loss-aDMPs preferentially at chromatin states marking active genomic regions occurred in non-CGI regions (OR 1.3–9.1, P < 0.0001, (Fig. 4c). In 5 of 7 tissues, an enrichment was found for Fig. 4a) in the seven tissues and in whole blood (Addi- Enhancers including in brain (OR 6.6, P < 0.0001, Fig. 4c), tional file 10: Fig. S8D), in line with previous reports [1, buccal cells (OR 2.7, P < 0.0001), liver (OR 1.6, P < 0.001), 16, 25]. Loss-aDMPs were even more tissue-specific than monocytes (OR 2.9, P < 0.0001) and Th cells (OR 11.2, gain-aDMPs: 4,176 loss-aDMPs (97.4%) were unique for P < 0.001). For 2 tissues, an enrichment for Genic enhanc- one tissue and only 111 loss-aDMPs (2.6%) were found ers was observed including brain (OR 11.9, P < 0.0001) a c b d Fig. 4 Characterization of loss-aDMPs. a Percentage and odds ratios of aDMPs in CGIs, shores and non-CGIs. Blue enriched, red depleted, grey non-significant. b Number of tissues an aDMPs was identified in. c Enrichment of gain-aDMPs in chromatin segmentations expressed as an odds ratio, grey non-significant enrichment. d Frequency of genes (y axis) against the number of tissues genes were identified in (x axis). e Expression of genes (y axis, RPKM) near loss-aDMPs per tissue (x axis). Abbreviations: TssA active TSS, TssAFlnk flanking active TSS, TxFlnk transcr. at gene 5′ and 3′, Tx strong transcription, TxWk weak transcription, EnhG genic enhancers, Enh enhancers, ZNF/Rpts ZNF genes + repeats, Het heterochromatin, TssBiv bivalent/poised TSS, BivFlnk flanking bivalent TSS/Enh, EnhBiv bivalent enhancer, ReprPC repressed polycomb, ReprPCWk weak repressed polycomb, Quies quiescent/low Slieker et al. Epigenetics & Chromatin (2018) 11:25 Page 7 of 11 and buccal (OR 3.6, P < 0.0001). Moreover, loss-aDMPs were enriched for active regions, including enhanc- were overrepresented at actively transcribed regions, ers. Underscoring the tissue-specificity of aDMPs, we including Transcribed at 3′and 5′ in brain (OR 14.5, observed that the large majority of both CGIs and genes P < 0.0001), buccal (OR 3.0, P < 0.0001) and monocytes with at least one aDMP were observed in a single tissue (OR 4.6, P < 0.01). Again, these observations were compa- only. rable with enrichments for aDMPs in whole blood (Addi- Our results raise the question of what mechanism tional file 10: Fig. S8E). drives the age-related DNA methylation changes. Despite Mapping loss-aDMPs to their nearest gene showed the tissue-specificity of individual aDMPs, in all tis - that the majority of genes uniquely occurred in a single sues it was the same type of functional genomic region tissue (2035 genes, 83.6%, Fig. 4d). The relatively few that accumulated aDMPs. We were able to exclude dif- genes found in multiple tissues included CD46 observed ferences in the number of stem cell divisions between in 6 out of 7 tissues and KCNQ1, FAM92B, PLEC, GSE1, tissues as a potential explanation. In line with our find - BAIAP2, PRDM16 and ACTG1 found in 4 tissues (Addi- ings for gain-aDMPs, age-related changes at regions tional file 14: Table S6). Many of these genes have a marked by polycomb have been found in many studies ‘housekeeping’ function. For example, PLEC, BAIAP2, investigating blood [7, 9, 10, 28] and have been found in ACTG1 play a role in the maintenance of the cytoskel- other species [17, 29]. A previously proposed explana- eton. The tissue-specificity of loss-aDMP genes was cor - tion for the gain of DNA methylation in CpG islands is roborated when counting the number of loss-aDMPs per by loss of binding—or erosion—of the polycomb repres- gene. For example, 24 loss-aDMPs were identified near sive complex 2 protein from the DNA (PRC2) . CGI DIP2C in the brain, against low numbers in other tis- promoters of developmental genes that are expressed at sues (buccal 1, liver 3, kidney 0, monocytes 0, SAT 0, Th low levels are kept in a repressive state primarily by the cell 0). In buccal, 18 CpGs lost DNA methylation near repressive complex PRC2 of which EHZ2 is a key com- SLC7A5, while no loss-aDMPs were found near this gene ponent. Age-related loss of repression would allow DNA in other tissues (Additional file 15: Table S7). methyltransferases (DNMTs) to de novo methylate CGIs Only loss-aDMP genes in brain showed enrichment . This explanation, however, requires a region and tis - for specific biological processes, including regulation of sue-specific mechanism that renders a subset of regions Small GTPase-mediated signal transduction (43 genes, more susceptible to age-related erosion of PRC2, which P < 0.0001) and Regulation of cell motion (33 genes, is currently unknown. While the genomic annotations bonf P < 0.05, Additional file 16: Table S8). For the other tis- that show age-related gain across tissues were the same, bonf sues, similar processes related to intracellular signalling the actual loci were tissue-specific, suggesting common and cell motility were overrepresented (P < 0.05). underlying mechanisms that link to tissue-specific age- Finally, we investigated the expression of genes near related changes. loss-aDMPs. Their expression levels were moderate In contrast to gain-aDMPs, loss-aDMPs overlapped (Fig. 4e), but no evidence was observed for age-related with active genomic regions, such as enhancers, corrob- changes (Additional file 11: Fig. S7C). Also, only a limited orating earlier studies in whole blood and mesenchymal overlap was found with previously identified age-related stem cells [10, 13, 27]. Despite their tissue-specificity, differentially expressed genes in whole blood (brain 114; genes near loss-aDMPs were not enriched for features buccal 77, liver 23, kidney 3, monocyte 36, SAT 0, Th cell conveying a role in tissue-specific processes, but instead 1) . with generic processes such as intracellular signalling cascade and cell motility pathways in line with findings in Discussion whole blood . Using genome-wide DNA methylation data on a large Remarkably, we did not find evidence for age-related number of individuals and 16 tissues, we report a cata- changes in expression of genes near aDMPs. This con - logue of 7850 robust aDMPs, 92% of which had not been firms previous studies that aDMPs, including ELOVL2, previously reported in studies of whole blood, and show have limited functional consequences [3, 9]. An explana- that age-related changes in DNA methylation are highly tion for the consistent increase in DMPs near ELOVL2 tissue-specific. The exceptions to this are well-known and other age-related DNA methylation changes could be CpGs in the ELOVL2 promoter that display an exception- due to underlying mitotic changes, although this has not ally consistent increase in DNA methylation with age in been observed in previous studies suggesting other mech- all tissues studied here . Age-related gain of DNA anisms driving age-related DNA methylation changes [31, methylation (gain-aDMPs) accumulated at CpG islands 32]. In contrast to aDMPs, CpGs accumulating variability and their flanking regions that were bound by the repres - in the population with age (aVMPs) are commonly asso- sive PRC2 component EZH2. In contrast, loss-aDMPs ciated with gene expression changes and may be more Slieker et al. Epigenetics & Chromatin (2018) 11:25 Page 8 of 11 informative for biological age . Nonetheless, age- elements involved are highly consistent across tissues. related epigenetic changes show resemblance with the Gain of methylation occurs at CGIs repressed by PRC2, changes seen in cancer and cellular senescence . For while loss of methylation accumulates at regions with example, the number of passages in vitro can be tracked active histone marks. Our findings indicate that the pre - based on the changes that occur at the DNA methylation cision of age predictors based on DNA methylation will level . Moreover, cellular senescence is associated with depend on whether the tissue of interest was among the hypermethylation of CGIs and flanking regions, while tissues on which set the predictor was trained on. Our hypomethylation occurs at non-CGI features [34, 35]. catalogue of tissue-specific aDMPs may guide the devel - Cancer is also characterized by hypermethylation of CpG opment of more precise predictors of chronological and islands and global hypomethylation [36, 37]. Here, we perhaps eventually provide insight into the tissue-specific observed a higher fraction of CpGs to gain DNA meth- differences in the mechanisms underlying ageing. ylation with time as compared with loss. However, this is likely due to the bias of the 450 k array towards CpG-rich Methods regions. After all, in a previous study comparing whole- Datasets genome bisulphite sequencing DNA methylation data of Datasets used in this study are summarized in Additional newborn versus a centenarian observed a much higher file 1: Table S1 and were obtained from the Gene Expres- fraction of CpGs to be hypomethylated in the centenarian sion Omnibus or ArrayExpress. For each of the datasets, than hypermethylated . normalized data or raw IDAT files were obtained. IDAT A limitation of our study is that not all datasets were files of DLPFC samples (age range 0–97 years) were equally sized. Larger datasets will have higher statistical downloaded (GEO accession number: GSE74193). Ini- power to detect aDMPs with smaller effect sizes. How - tial QC was performed using the R package MethylAid ever, we showed that there was no relationship between . Raw data underwent quality control using a cus- sample size and the tissue-specific character of aDMPs as tom pipeline (for more details see https ://git.lumc.nl/ this was preserved if aDMPs were identified only based molep i/Leide n450K ). Briefly, data were normalized using on effect size, in equally sized datasets, or in 16 instead functional normalization (minfi ), and probes were set to of 7 tissues. Also, one would expect at least to find an missing if ambiguously mapped, had a high detection P overlap between the strongest associated aDMPs, but value (> 0.01), low bead count (< 3 beads) or low success this was not the case. Inspection of effect sizes showed rate (missing in > 95% of the samples). Normalized data that CpGs detected as aDMPs in one tissue commonly of buccal (GEO accession number: GSE50759, normali- showed little or no evidence for an age-related change in zation: SWAN method on M values) consisted of 1202 DNA methylation in other tissues. However, larger stud- individuals with an age range of 1–28 years. Liver data ies are required to definitely exclude smaller effects in consisted of 147 individuals with age range between 15 other tissues. and 86, normalized data of 56 individuals (GEO accession Another limitation of our study is that the age ranges number: GSE48325, normalization using control probes), across the different tissues were different, which will have normalized data of 32 individuals (GEO accession num- influenced the number of aDMPs detected. Age-related ber: GSE61258, normalization using control probes), DNA methylation changes are known to accumulate IDAT files (Level 1) of 30 samples from TCGA and IDAT faster during adolescence than in adulthood [38, 39]. This files of 29 samples were kindly provided by the authors may have contributed the identification of the largest (GSE60753). Given that the liver dataset consisted of number of aDMPs in the smallest dataset, namely buc- dataset from different origins, we carefully inspected for cal (n = 96; age range 1–28). Finally, our results may be batch effects influencing the age-related changes. The influenced by measured and unmeasured confounding, first principal components associated with study, sex such as smoking, BMI, ethnicity and shifts in cell hetero- and age. To limit the effect of data origin, study ID was geneity . Some of the changes identified here may also added to the model. IDAT files (Level 1) of kidney con - be the result of a shift in cellular composition of a tissue sisted of 171 individuals with an age range between 15 with age, although we adjusted for cellular heterogene- and 86 and were obtained from TCGA. Normalized data ity in brain (neuronal and non-neuronal), monocytes, Th of monocytes (GEO accession number: GSE56046, nor- cells (residual impurities) and blood. malization: quantile normalization per colour signal and probe type) consisted of 1,202 individuals with an age Conclusion range of 44–83 years. Normalized data of Th cells (GEO Together, our results show that while the individual accession number: GSE56047, normalization: quantile CpGs that exhibit age-related differential methylation normalization) consisted of 214 individuals with an age are highly tissue-specific, the type of functional genomic range of 45–79 years). Normalized data of subcutaneous Slieker et al. Epigenetics & Chromatin (2018) 11:25 Page 9 of 11 fat (Array Expression accession number: E-MTAB-1866, tissue datasets with the size equal to the smallest data- quantile normalized per probe type) consisted of 648 set (N = 96). aDMPs were identified on these equally individuals with an age range of 39–85 years. Normal- sized datasets with both an effect size (> 2%/10 years) ized data of multiple brain regions consisted between and P value criterion (P ≤ 0.05). bonf 25 and 41 individuals with an age range between 15 and 114 years (GEO accession number: GSE64509, normali- zation using control probes). Raw IDAT files of epider - Annotations mis and dermis consisted 38 and 40 individuals between CpGs were mapped to CpG islands (UCSC), shores 20 and 90 years (GEO accession number: GSE52980). (2-kb regions flanking regions) and non-CGI described Normalized data of skeletal muscle consisted of 48 indi- previously . Chromatin state segmentations were viduals with an age range between 18 and 89 years (GEO obtained from the Epigenomics Roadmap. For each tis- accession number: GSE50498, normalization: quantile sue studied, the same tissue or the closest analogue was normalization on M values). Raw IDAT files of thyroid used from the Roadmap data. For the DLPFC, E073/ were kindly provided by the authors and consisted of DLPFC was used; buccal, E058/keratinocyte foreskin; 28 individuals between 23 and 81 years (GEO accession liver, E066/liver; kidney, E086/foetal kidney; SAT, E063/ number: GSE53051). Adipose nuclei; monocytes, E029/Monocytes; Th cells, E043/Th cells. For blood, functionality of a certain regions was based on the most frequent occurring fea- Blood dataset ture in primary blood cell subtypes. ChIP-seq data of DNA methylation blood data consisted of 3,295 from EZH2 was obtained for all cell types from the ENCODE six Dutch biobanks, previously described . Briefly, project. Enrichments were expressed as odds ratio, and data were normalized using functional normalization P value were calculated using a Chi-squared test. GO (R package minfi) using five principal components , enrichment was performed using the default settings poorly performing and ambiguously mapped CpGs were of DAVID using nearest genes (UCSC, 3′ or 5′ end of removed as well as CpGs on the sex chromosomes. Com- genes closest to the CpG) of aDMPs . bat was used to remove residual batch effects . Additional files Gene expression Gene counts were obtained from GTEX for frontal cor- Additional file 1: Table S1. Number of individuals used per tissue in this tex (for brain-aDMPs measured in DLPFC), oesophagus study. mucosa (for buccal-aDMPs), liver, kidney cortex and Additional file 2: Table S2. Identified aDMPs per tissue. whole blood (for Th cell-aDMPs and monocyte-aDMPs). Additional file 3: Figure S1. A Number of aDMPs (y axis) in our study The package cqn was used to normalize for GC content against the previously reported number of stem cell divisions per year (x axis) . B Number of aDMPs (y axis) against the sample size (x axis). and gene length. Normalized data was used to calculate Additional file 4: Table S3. Number of tissues a gene near gain-aDMPs the average RPKM per tissue and gene. was found. Additional file 5: Figure S2. A Heatmap of slopes of aDMPs identified Statistical analysis with only an effect size criterion. B Heatmap of slopes of aDMPs identified aDMPs were identified using linear regression between in equally sized datasets comprising randomly selected 96 individuals. Scale represents the change in DNA methylation in %/10 years. C Number DNA methylation and age, with adjustment for covari- of significant (P < 0.001) aDMPs in the other tissues in the equally-sized ates (sex, gender (all but SAT, females only), dataset datasets. (liver), tissue cell composition (DLPFC, monocytes, Additional file 6: Figure S3. Heatmap of slopes of age-related DNA Th cells, whole blood). For monocytes and Th cells, methylation in 16 tissues. Scale represents the change in DNA methyla- residual cell impurities were included in the model. For tion in %/10 years. whole blood, blood cell fractions were included as pre- Additional file 7: Figure S4. Overlap between gain- and loss-aDMPs and the CpGs in Horvath’s clock. viously described . Age was included in the model Additional file 8: Figure S5. Chronological age against the Horvath’s as a non-transformed numeric variable. aDMPs were predicted age for each of the 16 tissues. used in subsequent analyses if the slope was higher Additional file 9: Figure S6. Volcano plots of all CpGs per tissue, age- than 2% gain or loss per 10 years and if the Bonferroni related change (x axis) versus P value (y axis). CpGs from Horvath’s age adjusted P value reached significance (P ≤ 0.05). bonf predictor are marked in blue. To investigate the relation between power and the Additional file 10: Figure S8. A Percentage (top) and odds ratios observed tissue-specific character of aDMPs, we identi - (bottom) of gain-aDMPs in CGIs, shores and non-CGIs. Blue enriched, red depleted, grey non-significant. B Percentage (top) and odds ratios fied aDMPs solely based on the effect size (age-related (bottom) of aDMPs in EZH2 binding sites in the seven tissues plus whole slope > 2%/10 year, no P value cut-off). Secondly, a blood (ChIP-seq, any cell type, ENCODE). Blue enriched, red depleted, grey random set of individuals was drawn from each of the Slieker et al. Epigenetics & Chromatin (2018) 11:25 Page 10 of 11 Funding non-significant. C Enrichment of gain-aDMPs in chromatin segmenta- This study has been funded by the European Union’s Seventh Framework tions expressed in the seven tissues plus whole blood as an odds ratio, Program IDEAL (FP7/2007-2011) under Grant Agreement No. 259679 and grey non-significant. D Percentage (top) and odds ratios (bottom) of the BBSRC (BBI025751/1 and BB/I025263/1). The MRC Integrative Epidemi- loss-aDMPs in CGIs, shores and non-CGIs. Blue enriched, red depleted, ology Unit receives funding from the UK Medical Research Council (MC_ grey non-significant. E Enrichment of loss-aDMPs in chromatin segmenta- UU_12013/2, MC_UU_12013/8). tions expressed in the seven tissues plus whole blood as an odds ratio, grey non-significant. Abbreviations: TssA, Active TSS; TssAFlnk, Flanking Publisher’s Note active TSS; TxFlnk, Transcr. at gene 5′ and 3′; Tx, Strong transcription; TxWk, Springer Nature remains neutral with regard to jurisdictional claims in pub- Weak transcription; EnhG, Genic enhancers; Enh, Enhancers; ZNF/Rpts, lished maps and institutional affiliations. ZNF genes + repeats; Het, Heterochromatin; TssBiv, Bivalent/Poised TSS; BivFlnk, Flanking bivalent TSS/Enh; EnhBiv, Bivalent enhancer; ReprPC, Received: 21 December 2017 Accepted: 21 May 2018 Repressed Polycomb; ReprPCWk, Weak repressed Polycomb, Quies, Quiescent/low. Additional file 11: Figure S7. A Percentage (top) and enrichment (odds ratio, bottom) for CGI and the polycomb protein EZH2 and the combina- tion. B Expression (y axis, RPKM) of genes near gain- and loss-aDMPs for References each tissue for each age category (x axis). 1. Johansson Å, Enroth S, Gyllensten U. Continuous aging of the Additional file 12: Table S4. Frequency of gain-aDMPs near genes. human DNA methylome throughout the human lifespan. PLoS ONE. 2013;8:e67378. Additional file 13: Table S5. Enriched GO terms for gain-aDMPs per 2. McClay JL, Aberg KA, Clark SL, Nerella S, Kumar G, Xie LY, Hudson AD, tissue. Harada A, Hultman CM, Magnusson PK. A methylome-wide study of Additional file 14: Table S6. Number of tissues a gene near loss-aDMPs aging using massively parallel sequencing of the methyl-CpG-enriched was found. genomic fraction from blood in over 700 subjects. Hum Mol Genet. 2014;23:1175–85. Additional file 15: Table S7. Frequency of loss-aDMPs near genes. 3. Steegenga WT, Boekschoten MV, Lute C, Hooiveld GJ, de Groot PJ, Morris Additional file 16: Table S8. Enriched GO terms for loss-aDMPs per TJ, Teschendorff AE, Butcher LM, Beck S, Müller M. Genome-wide age- tissue. related changes in DNA methylation and gene expression in human PBMCs. Age. 2014;36:1523–40. 4. Garagnani P, Bacalini MG, Pirazzini C, Gori D, Giuliani C, Mari D, Di Blasio AM, Gentilini D, Vitale G, Collino S. Methylation of ELOVL2 gene as a new Abbreviations epigenetic marker of age. Aging Cell. 2012;11:1132–4. aDMPs: age-related differentially methylated positions; BivFlnk: flanking 5. Florath I, Butterbach K, Müller H, Bewerunge-Hudler M, Brenner H. Cross- bivalent transcription start site or enhancer; DLPFC: dorsolateral prefrontal sectional and longitudinal changes in DNA methylation with age: an cortex; Enh: enhancers; EnhBiv: bivalent enhancer; EnhG: genic enhancers; epigenome-wide analysis revealing over 60 novel age-associated CpG Het: heterochromatin; SAT: subcutaneous fat; Th cells: T helper cells; PcG: sites. Hum Mol Genet. 2014;23:1186–201. polycomb group; PRC2: polycomb repressive complex 2; Quies: quiescent/ 6. Marttila S, Kananen L, Häyrynen S, Jylhävä J, Nevalainen T, Hervonen A, low; ReprPC: repressed polycomb; ReprPCWk: weak repressed polycomb; TssA: Jylhä M, Nykter M, Hurme M. Ageing-associated changes in the human active TSS; TssAFlnk: flanking active TSS; TssBiv: bivalent/poised transcription DNA methylome: genomic locations and effects on gene expression. start site; Tx: strong transcription; TxFlnk: transcribed at gene 5′ and 3′; TxWk: BMC Genom. 2015;16:179. weak transcription; ZNF/Rpts: zinc fingers genes and repeats. 7. Rakyan VK, Down TA, Maslau S, Andrew T, Yang T-P, Beyan H, Whit- taker P, McCann OT, Finer S, Valdes AM. Human aging-associated DNA Authors’ contributions hypermethylation occurs preferentially at bivalent chromatin domains. RCS, BTH designed the study. RCS peformed the analyses. RCS, BTH, CR, TG Genome Res. 2010;20:434–9. contributed to methodology. RCS, BTH contributed to investigation. RCS, BTH 8. Bell JT, Tsai P-C, Yang T-P, Pidsley R, Nisbet J, Glass D, Mangino M, Zhai G, wrote the original draft. RCS, BTH, CR, TG, PES helped in writing review and Zhang F, Valdes A. Epigenome-wide scans identify differentially methyl- editing. All authors read and approved the final manuscript. ated regions for age and age-related phenotypes in a healthy ageing population. PLoS Genet. 2012;8:e1002629. Author details 1 9. Yuan T, Jiao Y, de Jong S, Ophoff RA, Beck S, Teschendorff AE. An integra- Molecular Epidemiology, Department of Biomedical Data Sciences, Leiden tive multi-scale analysis of the dynamic DNA methylation landscape in University Medical Center, Einthovenweg 20, 2333 ZC Leiden, The Netherlands. 2 aging. PLoS Genet. 2015;11:e1004996. MRC Integrative Epidemiology Unit, School of Social and Community Medi- 10. Dozmorov MG. Polycomb repressive complex 2 epigenomic signature cine, University of Bristol, Bristol BS8 2BN, UK. defines age-associated hypermethylation and gene expression changes. Epigenetics. 2015;10(6):484–95. Acknowledgements 11. Rönn T, Volkov P, Gillberg L, Kokosar M, Perfilyev A, Jacobsen AL, Jør - Not applicable. gensen SW, Brøns C, Jansson P-A, Eriksson K-F. Impact of age, BMI and HbA1c levels on the genome-wide DNA methylation and mRNA expres- Competing interests sion patterns in human adipose tissue and identification of epigenetic The authors declare that they have no competing interests. biomarkers in blood. Hum Mol Genet. 2015;24:3792–813. 12. Hernandez DG, Nalls MA, Gibbs JR, Arepalli S, van der Brug M, Chong S, Availability of data and materials Moore M, Longo DL, Cookson MR, Traynor BJ. Distinct DNA methylation All datasets used are publically available in GEO under accession numbers changes highly correlated with chronological age in the human brain. E-MTAB-1866, GSE48325, GSE50498, GSE50759, GSE52980, GSE53051, SE55763, Hum Mol Genet. 2011;20:1164–72. GSE56047, GSE56047, GSE60753, GSE61258, GSE64509, GSE74193, GTEX 13. Fernández AF, Bayón GF, Urdinguio RG, Toraño EG, García MG, Carella A, (www.gtexp ortal .org/home), and TCGA (https ://cance rgeno me.nih.gov). Petrus-Reurer S, Ferrero C, Martinez-Camblor P, Cubillo I. H3K4me1 marks DNA regions hypomethylated during aging in stem and differentiated Consent for publication cells. Genome Res. 2015;25(1):27–40. Not applicable. 14. Bekaert B, Kamalandua A, Zapico SC, Van de Voorde W, Decorte R. Improved age determination of blood and teeth samples using a Ethics approval and consent to participate selected set of DNA methylation markers. Epigenetics. 2015;10:922–30. All patients provided consent as described in the respective articles. Slieker et al. Epigenetics & Chromatin (2018) 11:25 Page 11 of 11 15. Bacalini MG, Deelen J, Pirazzini C, De Cecco M, Giuliani C, Lanzarini C, 32. Zhou W, Dinh HQ, Ramjan Z, Weisenberger DJ, Nicolet CM, Shen H, Laird Ravaioli F, Marasco E, van Heemst D, Suchiman HED. Systemic age- PW, Berman BP. DNA methylation loss in late-replicating domains is associated DNA hypermethylation of ELOVL2 gene: in vivo and in vitro linked to mitotic cell division. Nat Genet. 2018;50(4):591–602. evidences of a cell replication process. J Gerontol A Biol Sci Med Sci. 33. Koch CM, Joussen S, Schellenberg A, Lin Q, Zenke M, Wagner W. Monitor- 2017;72(8):1015–23. ing of cellular senescence by DNA-methylation at specific CpG sites. 16. Gopalan S, Carja O, Fagny M, Patin E, Myrick JW, McEwen LM, Mah SM, Aging Cell. 2012;11:366–9. Kobor MS, Froment A, Feldman MW. Trends in DNA methylation with age 34. Koch CM, Reck K, Shao K, Lin Q, Joussen S, Ziegler P, Walenda G, Drescher replicate across diverse human populations. Genetics. 2017;206:1659–74. W, Opalka B, May T. Pluripotent stem cells escape from senescence- 17. Spiers H, Hannon E, Wells S, Williams B, Fernandes C, Mill J. Age-associated associated DNA methylation changes. Genome Res. 2013;23:248–59. changes in DNA methylation across multiple tissues in an inbred mouse 35. Cruickshanks HA, McBryan T, Nelson DM, VanderKraats ND, Shah PP, van model. Mech Ageing Dev. 2016;154:20–3. Tuyn J, Rai TS, Brock C, Donahue G, Dunican DS. Senescent cells harbour 18. Hannum G, Guinney J, Zhao L, Zhang L, Hughes G, Sadda S, Klotzle B, features of the cancer epigenome. Nat Cell Biol. 2013;15:1495–506. Bibikova M, Fan JB, Gao Y, et al. Genome-wide methylation profiles reveal 36. Timp W, Feinberg AP. Cancer as a dysregulated epigenome allowing quantitative views of human aging rates. Mol Cell. 2013;49:359–67. cellular growth advantage at the expense of the host. Nat Rev Cancer. 19. Weidner CI, Lin Q, Koch CM, Eisele L, Beier F, Ziegler P, Bauerschlag DO, 2013;13:497–510. Jöckel K-H, Erbel R, Mühleisen TW. Aging of blood can be tracked by DNA 37. Irizarry RA, Ladd-Acosta C, Wen B, Wu Z, Montano C, Onyango P, Cui H, methylation changes at just three CpG sites. Genome Biol. 2014;15:R24. Gabo K, Rongione M, Webster M. The human colon cancer methylome 20. Horvath S. DNA methylation age of human tissues and cell types. shows similar hypo-and hypermethylation at conserved tissue-specific Genome Biol. 2013;14:R115. CpG island shores. Nat Genet. 2009;41:178–86. 21. Christensen BC, Houseman EA, Marsit CJ, Zheng S, Wrensch MR, Wiemels 38. Kananen L, Marttila S, Nevalainen T, Kummola L, Junttila I, Mononen N, JL, Nelson HH, Karagas MR, Padbury JF, Bueno R. Aging and environmen- Kähönen M, Raitakari OT, Hervonen A, Jylhä M, et al. The trajectory of tal exposures alter tissue-specific DNA methylation dependent upon CpG the blood DNA methylome ageing rate is largely set before adulthood: island context. PLoS Genet. 2009;5:e1000602. evidence from two longitudinal studies. AGE. 2016;38:65. 22. Day K, Waite LL, Thalacker-Mercer A, West A, Bamman MM, Brooks JD, 39. Alisch RS, Barwick BG, Chopra P, Myrick LK, Satten GA, Conneely KN, Myers RM, Absher D. Differential DNA methylation with age displays both Warren ST. Age-associated DNA methylation in pediatric populations. common and dynamic features across human tissues that are influenced Genome Res. 2012;22:623–32. by CpG landscape. Genome Biol. 2013;14:R102. 40. van Iterson M, Tobi E, Slieker R, den Hollander W, Luijk R, Slagboom P, Hei- 23. Slieker RC, van Iterson M, Luijk R, Beekman M, Zhernakova DV, Moed jmans B. MethylAid: visual and interactive quality control of large Illumina MH, Mei H, van Galen M, Deelen P, Bonder MJ, et al. Age-related accrual 450 k data sets. Bioinformatics. 2014;30(23):3435–7. of methylomic variability is linked to fundamental ageing mechanisms. 41. Aryee MJ, Jaffe AE, Corrada-Bravo H, Ladd-Acosta C, Feinberg AP, Hansen Genome Biol. 2016;17:191. KD, Irizarry RA. Minfi: a flexible and comprehensive bioconductor pack - 24. Horvath S. DNA methylation age of human tissues and cell types. age for the analysis of infinium DNA methylation microarrays. Bioinfor - Genome Biol. 2013;14:1. matics. 2014;30(10):1363–9. 25. Heyn H, Li N, Ferreira HJ, Moran S, Pisano DG, Gomez A, Diez J, Sanchez- 42. Johnson WE, Li C, Rabinovic A. Adjusting batch effects in microar - Mut JV, Setien F, Carmona FJ. Distinct DNA methylomes of newborns and ray expression data using empirical Bayes methods. Biostatistics. centenarians. Proc Natl Acad Sci. 2012;109:10522–7. 2007;8:118–27. 26. Kundaje A, Meuleman W, Ernst J, Bilenky M, Yen A, Heravi-Moussavi A, 43. Slieker RC, Bos SD, Goeman JJ, Bovée J, Talens RP, van der Breggen R, Kheradpour P, Zhang Z, Wang J, Ziller MJ. Integrative analysis of 111 refer- Suchiman H, Lameijer E-W, Putter H, van den Akker EB. Identification and ence human epigenomes. Nature. 2015;518:317–30. systematic annotation of tissue-specific differentially methylated regions 27. Peters MJ, Joehanes R, Pilling LC, Schurmann C, Conneely KN, Powell J, using the Illumina 450 k array. Epigenet Chromatin. 2013;6:26. Reinmaa E, Sutphin GL, Zhernakova A, Schramm K. The transcriptional 44. Huang DW, Sherman BT, Lempicki RA. Systematic and integrative analysis landscape of age in human peripheral blood. Nat Commun. 2015;6:8570. of large gene lists using DAVID bioinformatics resources. Nat Protoc. 28. Teschendorff AE, Menon U, Gentry-Maharaj A, Ramus SJ, Weisenberger 2009;4:44–57. DJ, Shen H, Campan M, Noushmehr H, Bell CG, Maxwell AP. Age-depend- 45. Thompson RF, Atzmon G, Gheorghe C, Liang HQ, Lowes C, Greally JM, ent DNA methylation of genes that are suppressed in stem cells is a Barzilai N. Tissue-specific dysregulation of DNA methylation in aging. hallmark of cancer. Genome Res. 2010;20:440–6. Aging Cell. 2010;9:506–18. 29. Maegawa S, Hinkal G, Kim HS, Shen L, Zhang L, Zhang J, Zhang N, Liang S, 46. Stubbs TM, Bonder MJ, Stark A-K, Krueger F, von Meyenn F, Stegle O, Reik Donehower LA, Issa J-PJ. Widespread and tissue specific age-related DNA W. Multi-tissue DNA methylation age predictor in mouse. Genome Biol. methylation changes in mice. Genome Res. 2010;20:332–40. 2017;18:68. 30. Jung M, Pfeifer GP. Aging and DNA methylation. BMC Biol. 2015;13:7. 47. Tomasetti C, Vogelstein B. Variation in cancer risk among tissues can be 31. Vandiver AR, Irizarry RA, Hansen KD, Garza LA, Runarsson A, Li X, Chien AL, explained by the number of stem cell divisions. Science. 2015;347:78–81. Wang TS, Leung SG, Kang S. Age and sun exposure-related widespread genomic blocks of hypomethylation in nonmalignant skin. Genome Biol. 2015;16:80. Ready to submit your research ? Choose BMC and benefit from: fast, convenient online submission thorough peer review by experienced researchers in your ﬁeld rapid publication on acceptance support for research data, including large and complex data types • gold Open Access which fosters wider collaboration and increased citations maximum visibility for your research: over 100M website views per year At BMC, research is always in progress. Learn more biomedcentral.com/submissions
Epigenetics & Chromatin – Springer Journals
Published: May 30, 2018
It’s your single place to instantly
discover and read the research
that matters to you.
Enjoy affordable access to
over 18 million articles from more than
15,000 peer-reviewed journals.
All for just $49/month
Query the DeepDyve database, plus search all of PubMed and Google Scholar seamlessly
Save any article or search result from DeepDyve, PubMed, and Google Scholar... all in one place.
Get unlimited, online access to over 18 million full-text articles from more than 15,000 scientific journals.
Read from thousands of the leading scholarly journals from SpringerNature, Wiley-Blackwell, Oxford University Press and more.
All the latest content is available, no embargo periods.
“Hi guys, I cannot tell you how much I love this resource. Incredible. I really believe you've hit the nail on the head with this site in regards to solving the research-purchase issue.”Daniel C.
“Whoa! It’s like Spotify but for academic articles.”@Phil_Robichaud
“I must say, @deepdyve is a fabulous solution to the independent researcher's problem of #access to #information.”@deepthiw
“My last article couldn't be possible without the platform @deepdyve that makes journal papers cheaper.”@JoseServera