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Blocks identified in photoaged skin are more hypomethylated in squamous cell cancer (SCC). (A) Distribution of methylation values in 450k probes measuring CpGs within the identified age- and sun exposure-associated blocks. (B) Distribution of methylation values in 450k probes measuring CpGs outside of the identified age- and sun exposure-associated blocks. (C) Distribution of methylation values in 450k probes measuring CpGs within open sea regions and within the identified age- and sun exposure-associated blocks. (D) Distribution of methylation values in 450k probes measuring CpGs within open sea regions and outside the identified age- and sun exposure-associated blocks. (E) Heatmap showing mean methylation in blocks identified comparing O-exp and Y-pro epidermis. Samples and blocks are ordered by hierarchal clustering. Yellow/red indicate higher/lower methylation, respectively.
Source publication
Aging and sun exposure are the leading causes of skin cancer. It has been shown that epigenetic changes, such as DNA methylation, are well established mechanisms for cancer, and also have emerging roles in aging and common disease. Here, we directly ask whether DNA methylation is altered following skin aging and/or chronic sun exposure in humans.
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... each skin donor, a dermatologist evaluated their degree of appar- ent skin aging in sun-exposed and sun-protected regions using two established scales. Griffiths' photodamage age grading measures coarse and fine wrinkling, photopig- mentation and yellowing [34], and showed a significant correlation with mean block methylation in sun-exposed epidermal samples (R 2 = 0.61, P < 0.001; Figure S4A in Additional file 11). Much of the variation in this rela- tionship appears to be linked to the younger, sun- exposed samples obtained from the face, which appear to be more hypomethylated for a given age grade than the arm samples. ...
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... photo-protected skin aging scale, in contrast, was developed for sun- protected skin and measures more nuanced fine wrinkling [35]. This sun-protected scale showed far less pronounced correlation with block methylation in sun-protected sam- ples (R 2 = 0.16, P = 0.1; Figure S4B in Additional file 11), suggesting that the block hypomethylation correlates with clinical grading primarily in epidermal samples affected by sun exposure. Thus, methylation levels in O-exp versus Y- pro blocks decrease with chronic exposure, varying with the degree of clinically appreciable photoaging. ...
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... obtained seven SCC tissue samples and six normal skin samples from the same body sites as the SCC samples and analyzed DNA methylation using the 450k array. Strikingly, we observed hypomethylation of SCC samples compared with normal samples when examining probes within the identified chronic exposure blocks ( Figure 4A), which is not seen when examining probes outside of these regions ( Figure 4B). This differ- ence is seen even when examination is limited to probes within the constitutively methylated open sea probes (Figure 4C,D). ...
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... obtained seven SCC tissue samples and six normal skin samples from the same body sites as the SCC samples and analyzed DNA methylation using the 450k array. Strikingly, we observed hypomethylation of SCC samples compared with normal samples when examining probes within the identified chronic exposure blocks ( Figure 4A), which is not seen when examining probes outside of these regions ( Figure 4B). This differ- ence is seen even when examination is limited to probes within the constitutively methylated open sea probes (Figure 4C,D). ...
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... we observed hypomethylation of SCC samples compared with normal samples when examining probes within the identified chronic exposure blocks ( Figure 4A), which is not seen when examining probes outside of these regions ( Figure 4B). This differ- ence is seen even when examination is limited to probes within the constitutively methylated open sea probes (Figure 4C,D). For 221 out of 223 identified hypomethy- lated blocks, SCC samples had lower mean methylation than normal samples (difference in mean methylation noted in Additional file 3). ...
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... 221 out of 223 identified hypomethy- lated blocks, SCC samples had lower mean methylation than normal samples (difference in mean methylation noted in Additional file 3). Clustering of these data based on mean methylation within identified photoaging blocks distinguishes most SCC from normal samples ( Figure 4E). ...
Citations
... Skin and skin-specific cells, such as keratinocytes, have biological programming dependent on sex (Liang et al, 2017). DNA epigenetic modifications increase with age and sun exposure, ultimately affecting cellular programming (Vandiver et al, 2015). In aggregate, thorough documentation and controlling of age, sex, ethnicity, and body site for all human skin samples in data analyses (to the greatest degree possible), we can ensure that results represent mechanisms related to the disease state and not altered responses due solely to differences in basic demographic factors. ...
... In contrast, the original epigenetic clocks are derived from real DNAm datasets describing a real aging process that is thought to include both stochastic and nonstochastic elements. Importantly, for any given epigenetic clock, unmethylated regions become gradually blurred also appears to be largely stochastic in the sense that neighboring CpGs do not necessarily change synchronously or by the same amount 33,34,36 . Indeed, the recent study by Tarkhov et al. concluded that most age-associated DNAm changes are devoid of nonstochastic coregulatory patterns 33 . ...
DNA methylation clocks can accurately estimate chronological age and, to some extent, also biological age, yet the process by which age-associated DNA methylation (DNAm) changes are acquired appears to be quasi-stochastic, raising a fundamental question: how much of an epigenetic clock’s predictive accuracy could be explained by a stochastic process of DNAm change? Here, using DNAm data from sorted immune cells, we build realistic simulation models, subsequently demonstrating in over 22,770 sorted and whole-blood samples from 25 independent cohorts that approximately 66–75% of the accuracy underpinning Horvath’s clock could be driven by a stochastic process. This fraction increases to 90% for the more accurate Zhang’s clock, but is lower (63%) for the PhenoAge clock, suggesting that biological aging is reflected by nonstochastic processes. Confirming this, we demonstrate that Horvath’s age acceleration in males and PhenoAge’s age acceleration in severe coronavirus disease 2019 cases and smokers are not driven by an increased rate of stochastic change but by nonstochastic processes. These results significantly deepen our understanding and interpretation of epigenetic clocks.
... An interesting observation, however, is that normal cells accrue DNAm changes with exposure to environmental factors, including cancer risk factors, in a manner that is also age-independent [41]. For instance, DNAm changes derived by comparing normal tissue of exposed individuals to the normal tissue of agematched unexposed individuals, have been observed in association with smoking [42][43][44][45], inflammation [46,47], sunlight exposure [48], Helicobacter pylori infection [49,50], human papilloma virus (HPV) infection [51], obesity [52] and alcohol consumption [22,53,54], and interestingly, these CpGs are universally enriched for PRC2-marked sites and display strong overlap between cancer risk factors [41], a key observation we will return to in the next section. ...
Epigenetic changes are known to accrue in normal cells as a result of ageing and cumulative exposure to cancer risk factors. Increasing evidence points towards age-related epigenetic changes being acquired in a quasi-stochastic manner, and that they may play a causal role in cancer development. Here, I describe the quasi-stochastic nature of DNA methylation (DNAm) changes in ageing cells as well as in normal cells at risk of neoplastic transformation, discussing the implications of this stochasticity for developing cancer risk prediction strategies, and in particular, how it may require a conceptual paradigm shift in how we select cancer risk markers. I also describe the mounting evidence that a significant proportion of DNAm changes in ageing and cancer development are related to cell proliferation, reflecting tissue-turnover and the opportunity this offers for predicting cancer risk via the development of epigenetic mitotic-like clocks. Finally, I describe how age-associated DNAm changes may be causally implicated in cancer development via an irreversible suppression of tissue-specific transcription factors that increases epigenetic and transcriptomic entropy, promoting a more plastic yet aberrant cancer stem-cell state.
This article is part of a discussion meeting issue ‘Causes and consequences of stochastic processes in development and disease’.
... The predictions are significantly biased for the achondroplasia cohort, which is a purely technical phenomenon caused by shifted covariates. c,f,i, Principal component analysis (PCA) of DNAm samples shows no covariate shift between the training and testing splits of the same aging skin dataset [32] (c), a moderate covariate shift between the different aging skin datasets [32,33] (f), and a strong covariate shift between the aging skin dataset [32] and the in vitro fibroblast reprogramming dataset [36] (i). d,g,j, Histograms of beta values for individual DNAm sites demonstrating no shifts between the two subsets of data (d), moderate shifts between aging skin datasets from different studies (g), and strong shifts between the aging skin and reprogramming datasets (j). ...
... Representative sites for histograms d, g, j were chosen from the top-four sites ordered by their correlation with chronological age. Second, for two independent datasets of aging human skin [32,33], moderate covariate shift is evident from similar analysis (Fig. 2f,g), with the KS test indicating substantial differences in individual distributions ( Fig. 2h): 81% of sites are rejected by the test (i.e., have different distributions). On the other hand, a joint analysis of two aging mouse liver datasets [34,35] displays minimal covariate shift (1% of rejected CpGs,Extended Data Fig. 1d-f). ...
Epigenetic aging clocks have been widely used to validate rejuvenation effects during cellular reprogramming. However, these predictions are unfalsifiable, since the true biological age of reprogrammed cells remains inaccessible. We present a multifaceted analytical framework to consider rejuvenation predictions from the uncertainty perspective. We discover that DNA methylation profiles of reprogramming are not represented in the aging data used for clock training, which introduces high epistemic uncertainty in aging predictions. Moreover, predictions of different published clocks are poorly consistent with each other and suggest even zero or negative rejuvenation. We show that the high prediction uncertainty challenges the reliability of rejuvenation effects observed during in vitro reprogramming prior to pluripotency and throughout embryogenesis. Conversely, our method also reveals a significant age increase after in vivo reprogramming. We propose to include uncertainty estimation in future aging clocks to avoid the risk of misinterpreting the results of biological age prediction.
... lesions, and malignant lesions, as seen in research with whole skin, epidermis alone, nevi and their adjacent nonlesional skin, and individual melanocytes. Clonal proliferations, sequence variants in driver genes, sequence variant burden and signatures, CNAs, sequence variants in signaling pathways, and epigenetic and transcriptional changes form a complex web of increasing disorder from normal skin toward melanoma (Fowler et al, 2021;Muse et al, 2022;Stark et al, 2018;Tan et al, 2019;Tang et al, 2020;Vandiver et al, 2015). ...
... Thus, chronic photodamage is linked to methylation changes but intrinsic ageing is not; as much as 21% of the genome may be hypomethylated in the older, chronically photodamaged specimens. Cell cycle and proliferation pathways were also upregulated in the exposed older samples (Vandiver et al, 2015). ...
... Mouse experiments suggest that X-ray exposureinduced hypomethylation of the whole genome in hematopoietic tissue, liver, and breast may be a carcinogenic mechanism [111,112]. UV light is a potent mutagen that can directly or indirectly induce DNA damage and methylate DNA [113]. UVB radiation, the most energetic and mutagenic component of UV radiation, is directly absorbed by DNA, causing DNA methylation and inducing adjacent pyrimidine bases to form dimer photoproducts. ...
Today, the rapid development of science and technology and the rapid change in economy and society are changing the way of life of human beings and affecting the natural, living, working, and internal environment on which human beings depend. At the same time, the global incidence of cancer has increased significantly yearly, and cancer has become the number one killer that threatens human health. Studies have shown that diet, living habits, residential environment, mental and psychological factors, intestinal flora, genetics, social factors, and viral and non-viral infections are closely related to human cancer. However, the molecular mechanisms of the environment and cancer development remain to be further explored. In recent years, DNA methylation has become a key hub and bridge for environmental and cancer research. Some environmental factors can alter the hyper/hypomethylation of human cancer suppressor gene promoters, proto-oncogene promoters, and the whole genome, causing low/high expression or gene mutation of related genes, thereby exerting oncogenic or anticancer effects. It is expected to develop early warning markers of cancer environment based on DNA methylation, thereby providing new methods for early detection of cancers, diagnosis, and targeted therapy. This review systematically expounds on the internal mechanism of environmental factors affecting cancer by changing DNA methylation, aiming to help establish the concept of cancer prevention and improve people's health.
... Genome-wide DNA methylation profiling of healthy epidermis comparing sun-exposed to sun-protected samples from the same individual has previously captured successfully the effects of one such exposure, that of ultraviolet (UV) irradiation. 9 Similar DNA methylation changes of affected CpGs were identified following acute experimental UV exposure. 10 Aside from exposure, skin conditions and malignancies such as atopic dermatitis or melanoma are all characterized by distinct DNA methylation profiles. ...
... We observed a significant overlap between CpG sites previously reported as differentially methylated upon UV exposure and those identified by our work. 9,10 In line with previous work, chronic UV irradiation may contribute to epigenetic changes by causing widespread hypomethylation of DNA in healthy epidermis. 1,9 Additionally, we found a significant overlap with sites correlating to acute UV irradiation. ...
... 9,10 In line with previous work, chronic UV irradiation may contribute to epigenetic changes by causing widespread hypomethylation of DNA in healthy epidermis. 1,9 Additionally, we found a significant overlap with sites correlating to acute UV irradiation. 10 These data highlight the potential practicality and effectiveness of using tape stripping as an alternative and less invasive method for collecting accurate epigenetic data to classify the site of origin as UV exposed or not exposed in relation to UVR. ...
Background
Both aging, disease and malignant transformation of the skin are associated with changes in DNA methylation. So far mostly invasive methodologies such as biopsies have been applied towards collecting DNA methylation signatures. Tape-stripping offers a non-invasive option for skin diagnostics. It enables the easy but robust capture biological material, in large numbers of participants without the need for specialized medical personnel.
Objectives
To design and validate a methodology for non-invasive skin sample collection using tape-stripping for subsequent DNA methylation analysis.
Methods
A total of 175 participants were recruited and provided tape-stripping samples from a sun-exposed area, 92 of those provided matched tape-stripping samples from a sun-protected area, and an additional 5 provided matched skin-shave biopsies from the same area. Using enzymatic conversion and whole genome Illumina sequencing, we generated genome-wide DNA methylation profiles which we used to evaluate the feasibility of non-invasive data acquisition, to compare with established sampling approaches and to investigate biomarker identification for age and UV exposure.
Results
We found that tape-stripping samples show strong concordance in their global DNA methylation landscapes with those of conventional invasive biopsies. Moreover, we show sample reproducibility and consistent global methylation profiles in skin tape-stripping samples collected from different parts of the body. Using matched samples from sun-protected and sun-exposed areas of the body we were able to validate the capacity of our method to capture the effects of environmental changes and aging from a cohort covering various ages, ethnicities, and skin types. We present DNA methylation changes on the skin resulting from UV exposure and identify a significant age-related hypermethylation of CpG islands, with a pronounced peak effect at 50–55 years of age, including methylation changes in well-described markers of aging.
Conclusion
These data demonstrate the feasibility of using tape-stripping combined with whole genome sequencing as a non-invasive approach to measure DNA methylation changes in the skin. In addition, they outline a viable experimental framework for the use of skin tape-stripping, particularly when that is performed in large cohorts of patients to identify biomarkers of skin aging, UV damage, and possibly to track treatment response to therapeutic interventions.
... We investigated whether typical environmental stressors to tissue induce epigenetic information loss. To do so, we compared methylation data from sun-exposed and sun-protected skin samples of both dermis and epidermis, in young and old individuals (38). Of the 277 skin unique methylation sites, 275 were present on the dataset (GSE51954; N = 78). ...
Aging is a major risk factor for a plethora of diseases. The information theory of aging posits that epigenetic information loss, especially alterations in methylation patterns, serves as a principal driver of the aging process. Despite this, the connection between epigenetic information loss and disease has not been thoroughly investigated. In this study, we mapped tissue-unique methylation patterns in both healthy and pathologically diagnosed organs. Our findings revealed that in multiple diseases and tissues, including kidney in Chronic Kidney Disease (CKD), liver in liver diseases, and adipose in Type 2 Diabetes (T2D), methylation patterns degrade in a specific manner, regressing towards the mean form observed across the body. We interpret this as epigenetic information loss, where tissue-unique patterns erode. By contrast, in pancreas of T2D patients, methylation patterns diverge away from the mean. Information loss is not limited to diseases. Sun exposure, for instance, was associated with information loss in the epidermis, but not in the dermis. Age-related erosion of unique methylation patterns was also observed in brain and breast tissues, while the colon showed divergence. Our findings demonstrate that analyzing methylation patterns in tissue-unique sites can effectively distinguish between patients and healthy controls across a range of diseases. It also underscores the role of epigenetic information loss as a common feature in various pathological conditions.
Graphical abstract
Tissue unique methylation pattern regress toward the mean upon disease
A single methylation site, showing low methylation in the liver and high in every other tissue, becomes more methylated in diseased livers.
... This is because skin samples often have higher variability in methylation rates. This variability is believed to be due to the relative differences in UV exposure of the skin at the site biopsied [46][47][48][49] . Future investigations comparing the methylation rates of different skin sampling sites within the same animal, for example along the dorsum of the animal, which is constantly exposed to UV, or along the sides with less direct exposure, may be of value. ...
... hypomethylation with increased UV exposure as compared to sun-protected skin location has been observed in humans 47,54 . These differences may explain the apparent divergent patterns observed between cetacean (UV exposed) and pinniped skin (UV protected). ...
Age determination of wild animals, including pinnipeds, is critical for accurate population assessment and management. For most pinnipeds, current age estimation methodologies utilize tooth or bone sectioning which makes antemortem estimations problematic. We leveraged recent advances in the development of epigenetic age estimators (epigenetic clocks) to develop highly accurate pinniped epigenetic clocks. For clock development, we applied the mammalian methylation array to profile 37,492 cytosine-guanine sites (CpGs) across highly conserved stretches of DNA in blood and skin samples (n = 171) from primarily three pinniped species representing the three phylogenetic families: Otariidae, Phocidae and Odobenidae. We built an elastic net model with Leave-One-Out-Cross Validation (LOOCV) and one with a Leave-One-Species-Out-Cross-Validation (LOSOCV). After identifying the top 30 CpGs, the LOOCV produced a highly correlated (r = 0.95) and accurate (median absolute error = 1.7 years) age estimation clock. The LOSOCV elastic net results indicated that blood and skin clock (r = 0.84) and blood (r = 0.88) pinniped clocks could predict age of animals from pinniped species not used for clock development to within 3.6 and 4.4 years, respectively. These epigenetic clocks provide an improved and relatively non-invasive tool to determine age in skin or blood samples from all pinniped species.
... [4][5][6][7][8][9][10][11] The consequences of skin degradation have already been reported at both genomic and proteomic levels. [12][13][14][15][16] However, studies on biochemical alterations that result in metabolome changes in the skin are limited. 17 Since the skin is an active organ, studying the changes in its metabolomic profile is fundamentally important to understand the negative effects, such as oxidative stress, and improve repair mechanisms. ...
... 18 Genomics, transcriptomics, and proteomics have already been widely applied to the study of damaged skin. [12][13][14][114][115][116][117] However, skin research based on metabolomics data is still in its early stages. Therefore, it is often necessary to integrate these different omic methodologies to find reliable biomarkers to be applied in clinical practice. ...
Metabolomics can provide a readout of the biochemical and physiological state of a biological system. Gas chromatography coupled to mass spectrometry (GC-MS) has been widely applied for metabolomic analysis due to its numerous advantages, such as good sensitivity, high resolution, reproducibility, extensive database, lower acquisition cost and greater coverage. In addition, combined with efficient methods of sampling and sample preparation, the metabolomic analysis of damaged skin based on GC-MS can provide an important step toward elucidating several skin diseases. Based on this, this review presents a comprehensive overview of sampling, sample preparation, data processing and GC-MS analysis of metabolomic studies of damaged skin. Also, part of the biological interpretation of metabolites such as cis- and trans-urocanic acid (UCA) altered in photoexposed skin and lauric acid (C12:0) and palmitic acid (C16:0) in melanoma is discussed. Finally, to improve decision-making, a part of the integration of skin metabolomics with other omics sciences for the advancement of diagnosis is presented.
