Development of a forensically useful age prediction method based on DNA methylation analysis

RAMP2-AS1 Regulates Endothelial Homeostasis and Aging

The homeostasis of vascular endothelium is crucial for cardiovascular health and endothelial cell (EC) aging and dysfunction could negatively impact vascular function. Leveraging transcriptome profiles from ECs subjected to various stimuli, including time-series data obtained from ECs under physiological pulsatile flow vs. pathophysiological oscillatory flow, we performed principal component analysis (PCA) to identify key genes contributing to divergent transcriptional states of ECs. Through bioinformatics analysis, we identified that a long non-coding RNA (lncRNA) RAMP2-AS1 encoded on the antisense of RAMP2, a determinant of endothelial homeostasis and vascular integrity, is a novel regulator essential for EC homeostasis and function. Knockdown of RAMP2-AS1 suppressed RAMP2 expression and caused EC functional changes promoting aging, including impaired angiogenesis and increased senescence. Our study demonstrates an integrative approach to quantifying EC aging based on transcriptome changes, which also identified a number of novel regulators, including protein-coding genes and many lncRNAs involved EC functional modulation, exemplified by RAMP2-AS1.

Characterization of the effects of age and childhood maltreatment on ELOVL2 DNA methylation

DNA methylation of the elongation of very long chain fatty acids protein 2 (ELOVL2) was suggested as a biomarker of biological aging, while childhood maltreatment (CM) has been associated with accelerated biological aging. We investigated the association of age and CM experiences with ELOVL2 methylation in peripheral blood mononuclear cells (PBMC). Furthermore, we investigated ELOVL2 methylation in the umbilical cord blood mononuclear cells (UBMC) of newborns of mothers with and without CM. PBMC and UBMC were isolated from 113 mother-newborn dyads and genomic DNA was extracted. Mothers with and without CM experiences were recruited directly postpartum. Mass array spectrometry and pyrosequencing were used for methylation analyses of ELOVL2 intron 1, and exon 1 and 5' end, respectively. ELOVL2 5' end and intron 1 methylation increased with higher age but were not associated with CM experiences. On the contrary, overall ELOVL2 exon 1 methylation increased with higher CM, but these changes were minimal and did not increase with age. Maternal CM experiences and neonatal methylation of ELOVL2 intron 1 or exon 1 were not significantly correlated. Our study suggests region-specific effects of chronological age and experienced CM on ELOVL2 methylation and shows that the epigenetic biomarker for age within the ELOVL2 gene does not show accelerated biological aging years after CM exposure.

Artificial neural network, predictor variables and sensitivity threshold for DNA methylation-based age prediction using blood samples

Regression models are often used to predict age of an individual based on methylation patterns. Artificial neural network (ANN) however was recently shown to be more accurate for age prediction. Additionally, the impact of ethnicity and sex on our previous regression model have not been studied. Furthermore, there is currently no age prediction study investigating the lower limit of input DNA at the bisulfite treatment stage prior to pyrosequencing. Herein, we evaluated both regression and ANN models, and the impact of ethnicity and sex on age prediction for 333 local blood samples using three loci on the pyrosequencing platform. Subsequently, we trained a one locus-based ANN model to reduce the amount of DNA used. We demonstrated that the ANN model has a higher accuracy of age prediction than the regression model. Additionally, we showed that ethnicity did not affect age prediction among local Chinese, Malays and Indians. Although the predicted age of males were marginally overestimated, sex did not impact the accuracy of age prediction. Lastly, we present a one locus, dual CpG model using 25 ng of input DNA that is sufficient for forensic age prediction. In conclusion, the two ANN models validated would be useful for age prediction to provide forensic intelligence leads.

Age estimation using DNA methylation technique in forensics: a systematic review

Background

In addition to the DNA sequence, epigenetic markers have become substantial forensic tools during the last decade. Estimating the age of an individual from human biological remains may provide information for a forensic investigation. Age estimation in molecular strategies can be obtained by telomere length, mRNa mutation, or by sjTRECs but the accuracy is not sufficient in forensic practice because of high margin error.

Main body

One solution to this problem is to use DNA methylation methods. DNA methylation markers for tissue identification at age-associated CpG sites have been suggested as the most informative biomarkers for estimating the age of an unknown donor. This review aims to give an overview of DNA methylation profiling for estimating the age in cases of forensic relevance and the important aspects in determining the mean absolute deviation (MAD) or mean absolute error (MAE) of the estimated age. Online database searching was performed through PubMed, Scopus, and Google Scholar with keywords selected for forensic age estimation. Thirty-two studies were included in the review, with variable DNA samples but blood commonly as a source. Pyrosequencing and EpiTYPER were methods mostly used in DNA analysis. The MAD in the estimates from DNA methylation was about 3 to 5 years, which was better than other methods such as those based on telomere length or signal-joint T-cell receptor excision circles. The ELOVL2 gene was a commonly used DNA methylation marker in age estimation.

Conclusion

DNA methylation is a favorable candidate for estimating the age at the time of death in forensic profiling, with an uncertainty mean absolute deviation of about 3 to 5 years in the predicted age. The sample type, platform techniques used, and methods to construct age predictive models were important in determining the accuracy in mean absolute deviation or mean absolute error. The DNA methylation outcome suggests good potential to support conventional STR profiling in forensic cases.

A minimal number CpGs of ELOVL2 gene for a chronological age estimation using pyrosequencing

Chronological age estimation is an important piece of human identification used in forensic practice. Epigenetic modifications, especially DNA methylation, have been proposed to predict age. The methylation of the ELOVL2 gene is one of the age-related markers that could be tested in fresh or postmortem blood sample. We study the use of DNA methylation markers on the ELOVL2 gene and develop a prediction model to estimate the age from a postmortem blood sample using pyrosequencing. From 100 anonymous blood samples, a correlation study of DNA methylation and age was investigated. The regression analysis revealed 2 CpG sites for model prediction with an adjusted R² value of 0.7 (p < 0.01). The model explained 74% of the variation in postmortem blood samples (n = 36) with a prediction error (RMSE) of 10.2 years and a mean absolute deviation (MAD) of 7.1 years, whereas the model (excluding a younger age group) had improved with a RMSE of 5.6 years and a MAD of 4.2 years. The performance parameters were analyzed in several simulated models and indicated that these markers are advantageous for age estimation in forensic scenarios. Finally, a robustness and reproducibility of the pyrosequencing technique would enable this approach to be the part of an age prediction in forensic investigation.

HMGB1 Recruits TET2/AID/TDG to Induce DNA Demethylation in STAT3 Promoter in CD4+ T Cells from aGVHD Patients

STAT3 is highly expressed in aGVHD CD4⁺ T cells and plays a critical role in inducing or worsening aGVHD. In our preceding studies, DNA hypomethylation in STAT3 promoter was shown to cause high expression of STAT3 in aGVHD CD4⁺ T cells, and the process could be modulated by HMGB1, but the underlying mechanism remains unclear. TET2, AID, and TDG are indispensable in DNA demethylation; meanwhile, TET2 and AID also serve extremely important roles in immune response. So, we speculated these enzymes involved in the STAT3 promoter hypomethylation induced by HMGB1 in aGVHD CD4⁺ T cells. In this study, we found that the binding levels of TET2/AID/TDG to STAT3 promoter were remarkably increased in CD4⁺T cells from aGVHD patients and were significantly negatively correlated with the STAT3 promoter methylation level. Simultaneously, we revealed that HMGB1 could recruit TET2, AID, and TDG to form a complex in the STAT3 promoter region. Interference with the expression of TET2/AID/TDG inhibited the overexpression of STAT3 caused by HMGB1 downregulation of the STAT3 promoter DNA methylation. These data demonstrated a new molecular mechanism of how HMGB1 promoted the expression of STAT3 in CD4⁺ T cells from aGVHD patients.

Molecular basis of ageing in chronic metabolic diseases

AIM

Over the last decades, the shift in age distribution towards older ages and the progressive ageing which has occurred in most populations have been paralleled by a global epidemic of obesity and its related metabolic disorders, primarily, type 2 diabetes (T2D). Dysfunction of the adipose tissue (AT) is widely recognized as a significant hallmark of the ageing process that, in turn, results in systemic metabolic alterations. These include insulin resistance, accumulation of ectopic lipids and chronic inflammation, which are responsible for an elevated risk of obesity and T2D onset associated to ageing. On the other hand, obesity and T2D, the paradigms of AT dysfunction, share many physiological characteristics with the ageing process, such as an increased burden of senescent cells and epigenetic alterations. Thus, these chronic metabolic disorders may represent a state of accelerated ageing.

MATERIALS AND METHODS

A more precise explanation of the fundamental ageing mechanisms that occur in AT and a deeper understanding of their role in the interplay between accelerated ageing and AT dysfunction can be a fundamental leap towards novel therapies that address the causes, not just the symptoms, of obesity and T2D, utilizing strategies that target either senescent cells or DNA methylation.

RESULTS

In this review, we summarize the current knowledge of the pathways that lead to AT dysfunction in the chronological ageing process as well as the pathophysiology of obesity and T2D, emphasizing the critical role of cellular senescence and DNA methylation.

CONCLUSION

Finally, we highlight the need for further research focused on targeting these mechanisms.

Improvements and inter-laboratory implementation and optimization of blood-based single-locus age prediction models using DNA methylation of the ELOVL2 promoter

Several blood-based age prediction models have been developed using less than a dozen to more than a hundred DNA methylation biomarkers. Only one model (Z-P1) based on pyrosequencing has been developed using DNA methylation of a single locus located in the ELOVL2 promoter, which is considered as one of the best age-prediction biomarker. Although multi-locus models generally present better performances compared to the single-locus model, they require more DNA and present more inter-laboratory variations impacting the predictions. Here we developed 17,018 single-locus age prediction models based on DNA methylation of the ELOVL2 promoter from pooled data of four different studies (training set of 1,028 individuals aged from 0 and 91 years) using six different statistical approaches and testing every combination of the 7 CpGs, aiming to improve the prediction performances and reduce the effects of inter-laboratory variations. Compared to Z-P1 model, three statistical models with the optimal combinations of CpGs presented improved performances (MAD of 4.41–4.77 in the testing set of 385 individuals) and no age-dependent bias. In an independent testing set of 100 individuals (19–65 years), we showed that the prediction accuracy could be further improved by using different CpG combinations and increasing the number of technical replicates (MAD of 4.17).

A Comparison of Forensic Age Prediction Models Using Data From Four DNA Methylation Technologies

Individual age estimation can be applied to criminal, legal, and anthropological investigations. DNA methylation has been established as the biomarker of choice for age prediction, since it was observed that specific CpG positions in the genome show systematic changes during an individual’s lifetime, with progressive increases or decreases in methylation levels. Subsequently, several forensic age prediction models have been reported, providing average age prediction error ranges of ±3–4 years, using a broad spectrum of technologies and underlying statistical analyses. DNA methylation assessment is not categorical but quantitative. Therefore, the detection platform used plays a pivotal role, since quantitative and semi-quantitative technologies could potentially result in differences in detected DNA methylation levels. In the present study, we analyzed as a shared sample pool, 84 blood-based DNA controls ranging from 18 to 99 years old using four different technologies: EpiTYPER^®, pyrosequencing, MiSeq, and SNaPshot^TM. The DNA methylation levels detected for CpG sites from ELOVL2, FHL2, and MIR29B2 with each system were compared. A restricted three CpG-site age prediction model was rebuilt for each system, as well as for a combination of technologies, based on previous training datasets, and age predictions were calculated accordingly for all the samples detected with the previous technologies. While the DNA methylation patterns and subsequent age predictions from EpiTYPER^®, pyrosequencing, and MiSeq systems are largely comparable for the CpG sites studied, SNaPshot^TM gives bigger differences reflected in higher predictive errors. However, these differences can be reduced by applying a z-score data transformation.

Age prediction in living: Forensic epigenetic age estimation based on blood samples

DNA methylation analysis in a variety of genes has brought promising results in age estimation. The main aim of this study was to evaluate DNA methylation levels from four age-correlated genes, ELOVL2, FHL2, EDARADD and PDE4C, in blood samples of healthy Portuguese individuals. Fifty-three samples were analyzed through the bisulfite polymerase chain reaction (PCR) sequencing method for CpG dinucleotide methylation status. Linear regression models were used to analyze relationships between methylation levels and chronological age. The highest age-associated CpG in each locus was chosen to build a multi-locus age prediction model (APM), allowing to obtain a Mean Absolute Deviation (MAD) between chronological and predicted ages of 5.35 years, explaining 94.1% of age variation. Validation approaches demonstrated the accuracy and reproducibility of the proposed multi-locus APM. Testing the APM in 51 blood samples from deceased individuals a MAD of 9.72 years was obtained. Potential differences in methylation status between samples from living and deceased individuals could exist since the highest age-correlated CpGs were different in some genes between both groups. In conclusion, our study using the bisulfite PCR sequencing method is in accordance with the high age prediction accuracy of DNA methylation levels in four previously reported age-associated genes. DNA methylation pattern differences between blood samples from living and deceased individuals should be taken into account in forensic contexts.

DNA Methylation Clocks and Their Predictive Capacity for Aging Phenotypes and Healthspan

The number of age predictors based on DNA methylation (DNAm) profile is rising due to their potential in predicting healthspan and application in age-related illnesses, such as neurodegenerative diseases. The cumulative assessment of DNAm levels at age-related CpGs (DNAm clock) may reflect biological aging. Such DNAm clocks have been developed using various training models and could mirror different aspects of disease/aging mechanisms. Hence, evaluating several DNAm clocks together may be the most effective strategy in capturing the complexity of the aging process. However, various confounders may influence the outcome of these age predictors, including genetic and environmental factors, as well as technical differences in the selected DNAm arrays. These factors should be taken into consideration when interpreting DNAm clock predictions. In the current review, we discuss 15 reported DNAm clocks with consideration for their utility in investigating neurodegenerative diseases and suggest research directions towards developing a more optimal measure for biological aging.

Epigenetic clocks may come out of rhythm-implications for the estimation of chronological age in forensic casework

There is a growing perception that DNA methylation may be influenced by exogenous and endogenous parameters. Knowledge of these factors is of great relevance for the interpretation of DNA-methylation data for the estimation of chronological age in forensic casework. We performed a literature review to identify parameters, which might be of relevance for the prediction of chronological age based on DNA methylation. The quality of age predictions might particularly be influenced by lifetime adversities (chronic stress, trauma/post-traumatic stress disorder (PTSD), violence, low socioeconomic status/education), cancer, obesity and related diseases, infectious diseases (especially HIV and Cytomegalovirus (CMV) infections), sex, ethnicity and exposure to toxins (alcohol, smoking, air pollution, pesticides). Such factors may alter the DNA methylation pattern and may explain the partly high deviations between epigenetic age and chronological age in single cases (despite of low mean absolute deviations) that can also be observed with “epigenetic clocks” comprising a high number of CpG sites. So far, only few publications dealing with forensic age estimation address these confounding factors. Future research should focus on the identification of further relevant confounding factors and the development of models that are “robust” against the influence of such biological factors by systematic investigations under targeted inclusion of diverse and defined cohorts.

Evaluation of marker selection methods and statistical models for chronological age prediction based on DNA methylation

In forensic investigation, retrieving biological information from DNA evidence is a promising field of interest. One of the applications is on the estimation of the age of the donor based on DNA methylation. A large number of studies focused on age prediction using the 450 K Human Methylation Beadchip. Various marker selection methods and prediction models have been considered. However, there is a lack of research evaluating different high-dimensional variable selection methods of CpG sites with various models for age prediction. The aim of this study is to evaluate four variable selection methods (forward selection, LASSO, elastic net and SCAD) combined with a classical statistical model and sophisticated machine learning models based on the mean absolute deviation (MAD) and the root-mean-square error (RMSE). We used publicly available 450 K data set containing 991 whole blood samples (age 19-101 years). We found that the multiple linear regression model with 16 markers selected from the forward selection method performed very well in age prediction (MAD = 3.76 years and RMSE = 5.01 years). On the other hand, the highly advanced ultrahigh dimensional variable selection methods and sophisticated machine learning algorithms appeared unnecessary for age prediction based on DNA methylation.

The biological age of the heart is consistently younger than chronological age

Chronological age represents the main factor in donor selection criteria for organ transplantation, however aging is very heterogeneous. Defining the biological aging of individual organs may contribute to supporting this process. In this study we examined the biological age of the heart [right (RA)/left atrium (LA)] and peripheral blood leucocytes in the same subject, and compared these to assess whether blood mirrors cardiac biological aging. Biological aging was studied in 35 donors (0.4–72 years) by exploring mitotic and non-mitotic pathways, using telomere length (TL) and age-dependent methylation changes in certain CpG loci (DNAmAge). Heart non-mitotic DNAmAge was strongly younger than that of both blood (− 10 years, p < 0.0001) and chronological age (− 12 years, p < 0.0001). Instead, heart and blood mitotic age (TL) were similar, and there was no difference in DNAmAge and TL between RA and LA. DNAmAge negatively correlated with TL in heart and blood (p ≤ 0.01). Finally, blood and heart TL (p < 0.01) and DNAmAge (p < 0.0001) were correlated. Therefore, blood can be a proxy indicator of heart biological age. While future investigation on post-transplant graft performance in relation to biological aging is still needed, our study could contribute to opening up novel basic and clinical research platforms in the field of organ transplantation.

New targeted approaches for epigenetic age predictions

Background

Age-associated DNA methylation changes provide a promising biomarker for the aging process. While genome-wide DNA methylation profiles enable robust age-predictors by integration of many age-associated CG dinucleotides (CpGs), there are various alternative approaches for targeted measurements at specific CpGs that better support standardized and cost-effective high-throughput analysis.

Results

In this study, we utilized 4647 Illumina BeadChip profiles of blood to select CpG sites that facilitate reliable age-predictions based on pyrosequencing. We demonstrate that the precision of DNA methylation measurements can be further increased with droplet digital PCR (ddPCR). In comparison, bisulfite barcoded amplicon sequencing (BBA-seq) gave slightly lower correlation between chronological age and DNA methylation at individual CpGs, while the age-predictions were overall relatively accurate. Furthermore, BBA-seq data revealed that the correlation of methylation levels with age at neighboring CpG sites follows a bell-shaped curve, often associated with a CTCF binding site. We demonstrate that within individual BBA-seq reads the DNA methylation at neighboring CpGs is not coherently modified, but reveals a stochastic pattern. Based on this, we have developed a new approach for epigenetic age predictions based on the binary sequel of methylated and non-methylated sites in individual reads, which reflects heterogeneity in epigenetic aging within a sample.

Conclusion

Targeted DNA methylation analysis at few age-associated CpGs by pyrosequencing, BBA-seq, and particularly ddPCR enables high precision of epigenetic age-predictions. Furthermore, we demonstrate that the stochastic evolution of age-associated DNA methylation patterns in BBA-seq data enables epigenetic clocks for individual DNA strands.

Development and optimization of the VISAGE basic prototype tool for forensic age estimation

The VISAGE (VISible Attributes through GEnomics) consortium aims to develop, optimize and validate prototype tools to broaden the use of DNA intelligence methods in forensic routine laboratories. This includes age estimation based on the quantification of DNA methylation at specific CpG sites. Here, we present the VISAGE basic prototype tool for age estimation targeting 32 CpGs from five genes ELOVL2, MIR29B2CHG (herein, MIR29B2C), FHL2, TRIM59 and KLF14. The assay interrogates these well described age markers by multiplex PCR for bisulfite converted DNA and massively parallel sequencing on a MiSeq FGx instrument. We describe protocol optimizations including tests on five bisulfite conversion kits and an evaluation of the assay's reproducibility and sensitivity with artificially methylated DNA standards. We observed robust quantification of methylation levels with a mean standard deviation of 1.4 % across ratios. Sensitivity tests showed no increase of variability down to 20 ng DNA input into bisulfite conversion with a median difference below 1.6 % between technical replicates.

A New Robust Epigenetic Model for Forensic Age Prediction

Forensic DNA phenotyping refers to an emerging field of forensic sciences aimed at the prediction of externally visible characteristics of unknown sample donors directly from biological materials. The aging process significantly affects most of the above characteristics making the development of a reliable method of age prediction very important. Today, the so-called "epigenetic clocks" represent the most accurate models for age prediction. Since they are technically not achievable in a typical forensic laboratory, forensic DNA technology has triggered efforts toward the simplification of these models. The present study aimed to build an epigenetic clock using a set of methylation markers of five different genes in a sample of the Italian population of different ages covering the whole span of adult life. In a sample of 330 subjects, 42 selected markers were analyzed with a machine learning approach for building a prediction model for age prediction. A ridge linear regression model including eight of the proposed markers was identified as the best performing model across a plethora of candidates. This model was tested on an independent sample of 83 subjects providing a median error of 4.5 years. In the present study, an epigenetic model for age prediction was validated in a sample of the Italian population. However, its applicability to advanced ages still represents the main limitation in forensic caseworks.

DNA methylation age estimation in blood samples of living and deceased individuals using a multiplex SNaPshot assay

Many studies in the forensic field have reported that analysis of DNA methylation is the most reliable method of predicting age. In a previous study, 5 CpG sites located in ELOVL2, FHL2, KLF14, C1orf132 and TRIM59 genes were tested for age prediction purposes in blood, saliva and buccal swab samples from Korean individuals using a multiplex methylation SNaPshot assay. The main goals of the present study were i) to replicate the same multiplex SNaPshot assay in blood samples from Portuguese individuals, ii) to compare DNA methylation status between two different populations and iii) to address putative differences in the methylation status between blood from living and deceased individuals. Blood samples from 59 living individuals (37 females, 22 males; aged 1-94 years-old) and from 62 deceased individuals (13 females, 49 males; aged 28-86 years-old) were evaluated. The specific primers were those previously described. Linear regression models were used to analyse relationships between methylation levels and chronological age using IBM SPSS software v.24. Our results allowed to build a final age prediction model (APM) for blood samples of living individuals with 3 CpG sites, at ELOVL2, FHL2 and C1orf132 genes, explaining 96.3% of age variation, with a mean absolute deviation (MAD) from chronological age of 4.25 years. Some differences were found in the extent of the age association in the targeted loci comparing Portuguese with Korean individuals. The final APM built for deceased individuals included 4 CpG sites, at ELOVL2, FHL2, C1orf132 and TRIM59 genes, explaining 79.3% of age variation, with a MAD of 5.36 years. Combining both sets of samples from living and deceased individuals, the most accurate APM with 4 CpGs, at ELOVL2, FHL2, C1orf132 and TRIM59 genes, explained 92.5% of variation in age, with a MAD of 4.97 years. In conclusion, our study replicated in blood samples of Portuguese living individuals a previous SNaPshot assay for age estimation. The possibility that age markers might be population specific and that postmortem changes can alter the methylation status among specific loci was suggested by our data. Our study showed the usefulness of the multiplex methylation SNaPshot assay for forensic analysis in blood samples of living and deceased individuals.

One-year Mediterranean diet promotes epigenetic rejuvenation with country- and sex-specific effects: a pilot study from the NU-AGE project

Mediterranean diet has been proposed to promote healthy aging, but its effects on aging biomarkers have been poorly investigated. We evaluated the impact of a 1-year Mediterranean-like diet in a pilot study including 120 elderly healthy subjects from the NU-AGE study (60 Italians, 60 Poles) by measuring the changes in their epigenetic age, assessed by Horvath's clock. We observed a trend towards epigenetic rejuvenation of participants after nutritional intervention. The effect was statistically significant in the group of Polish females and in subjects who were epigenetically older at baseline. A genome-wide association study of epigenetic age changes after the intervention did not return significant (adjusted p value < 0.05) loci. However, we identified small-effect alleles (nominal p value < 10-4), mapping in genes enriched in pathways related to energy metabolism, regulation of cell cycle, and of immune functions. Together, these findings suggest that Mediterranean diet can promote epigenetic rejuvenation but with country-, sex-, and individual-specific effects, thus highlighting the need for a personalized approach to nutritional interventions.

DNA methylation levels and telomere length in human teeth: usefulness for age estimation

In the last decade, increasing knowledge of epigenetics has led to the development of DNA methylation-based models to predict age, which have shown high predictive accuracy. However, despite the value of teeth as forensic samples, few studies have focused on this source of DNA. This study used bisulfite pyrosequencing to measure the methylation levels of specific CpG sites located in the ELOVL2, ASPA, and PDE4C genes, with the aim of selecting the most age-informative genes and determining their associations with age, in 65 tooth samples from individuals 15 to 85 years old. As a second aim, methylation data and measurements of relative telomere length in the same set of samples were used to develop preliminary age prediction models to evaluate the accuracy of both biomarkers together and separately in estimating age from teeth for forensic purposes. In our sample, several CpG sites from ELOVL2 and PDE4C genes, as well as telomere length, were significantly associated with chronological age. We developed age prediction quantile regression models based on DNA methylation levels, with and without telomere length as an additional variable, and adjusted for type of tooth and sex. Our results suggest that telomere length may have limited usefulness as a supplementary marker for DNA methylation-based age estimation in tooth samples, given that it contributed little improvement in the prediction errors of the models. In addition, even at older ages, DNA methylation appeared to be more informative in predicting age than telomere length when both biomarkers were evaluated separately.

Individual DNA Methylation Profile is Correlated with Age and can be Targeted to Modulate Healthy Aging and Longevity

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Patterns of DNA methylation, the best characterized epigenetic modification, are modulated by aging. In humans, different studies at both site-specific and genome-wide levels have reported that modifications of DNA methylation are associated with the chronological aging process but also with the quality of aging (or biological aging), providing new perspectives for establishing powerful biomarkers of aging.

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In this article, the role of DNA methylation in aging and longevity has been reviewed by analysing literature data about DNA methylation variations occurring during the lifetime in response to environmental factors and genetic background, and their association with the aging process and, in particular, with the quality of aging. Special attention has been devoted to the relationship between nuclear DNA methylation patterns, mitochondrial DNA epigenetic modifications, and longevity. Mitochondrial DNA has recently been reported to modulate global DNA methylation levels of the nuclear genome during the lifetime, and, in spite of the previous belief, it has been found to be the target of methylation modifications.

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Analysis of DNA methylation profiles across lifetime shows that a remodeling of the methylome occurs with age and/or with age-related decline. Thus, it can be an excellent biomarker of aging and of the individual decline and frailty status. The knowledge about the mechanisms underlying these modifications is crucial since it might allow the opportunity for targeted treatment to modulate the rate of aging and longevity.

Systematic underestimation of the epigenetic clock and age acceleration in older subjects

Background

The Horvath epigenetic clock is widely used. It predicts age quite well from 353 CpG sites in the DNA methylation profile in unknown samples and has been used to calculate “age acceleration” in various tissues and environments.

Results

The model systematically underestimates age in tissues from older people. This is seen in all examined tissues but most strongly in the cerebellum and is consistently observed in multiple datasets. Age acceleration is thus age-dependent, and this can lead to spurious associations. The current literature includes examples of association tests with age acceleration calculated in a wide variety of ways.

Conclusions

The concept of an epigenetic clock is compelling, but caution should be taken in interpreting associations with age acceleration. Association tests of age acceleration should include age as a covariate.

MapReduce-Based Parallel Genetic Algorithm for CpG-Site Selection in Age Prediction

Genomic biomarkers such as DNA methylation (DNAm) are employed for age prediction. In recent years, several studies have suggested the association between changes in DNAm and its effect on human age. The high dimensional nature of this type of data significantly increases the execution time of modeling algorithms. To mitigate this problem, we propose a two-stage parallel algorithm for selection of age related CpG-sites. The algorithm first attempts to cluster the data into similar age ranges. In the next stage, a parallel genetic algorithm (PGA), based on the MapReduce paradigm (MR-based PGA), is used for selecting age-related features of each individual age range. In the proposed method, the execution of the algorithm for each age range (data parallel), the evaluation of chromosomes (task parallel) and the calculation of the fitness function (data parallel) are performed using a novel parallel framework. In this paper, we consider 16 different healthy DNAm datasets that are related to the human blood tissue and that contain the relevant age information. These datasets are combined into a single unioned set, which is in turn randomly divided into two sets of train and test data with a ratio of 7:3, respectively. We build a Gradient Boosting Regressor (GBR) model on the selected CpG-sites from the train set. To evaluate the model accuracy, we compared our results with state-of-the-art approaches that used these datasets, and observed that our method performs better on the unseen test dataset with a Mean Absolute Deviation (MAD) of 3.62 years, and a correlation (R²) of 95.96% between age and DNAm. In the train data, the MAD and R² are 1.27 years and 99.27%, respectively. Finally, we evaluate our method in terms of the effect of parallelization in computation time. The algorithm without parallelization requires 4123 min to complete, whereas the parallelized execution on 3 computing machines having 32 processing cores each, only takes a total of 58 min. This shows that our proposed algorithm is both efficient and scalable.

Novel multiplex strategy for DNA methylation-based age prediction from small amounts of DNA via Pyrosequencing

DNA methylation-based age estimation is a promising new tool for forensic molecular biology. There is growing understanding of the best predictive CpG loci and their performance in various sample types. Since forensic samples usually provide only small amounts of DNA, the sensitivity of the method is crucial. Pyrosequencing is one of the most sensitive methods but only capable to analyze different target regions separately. Thus, multiple input DNA samples are required for investigations of different target regions, which is required for all current age estimation models. To overcome this limitation, we developed a novel multiplex strategy for Pyrosequencing, which allows the investigation of different target regions from a single small amount of input DNA. A pre-amplification step was introduced to increase the amount of target-specific template for the subsequent sequencing PCR step. We tested this multiplex strategy for eight target regions including 15 age CpGs associated with the genes of ELOVL2, FHL2, CCDC102B, C1orf132, KLF14, EDARADD, PDE4C and SST. Except for FHL2, all target regions were successfully sequenced with the multiplex strategy and the precision in terms of reproducibility of the measurements was equal to the singleplex strategy. The measured methylation values at the age CpGs displayed borderline significant differences between both analytical strategies for six out of 14 CpG sites whereas both strategies delivered equal methylation values for the remaining eight age CpGs. In total, our results indicate that the multiplex strategy can act as a promising alternative for age estimation studies in cases when only limited amounts of DNA samples are available.

Modified aging of elite athletes revealed by analysis of epigenetic age markers

Recent progress in epigenomics has led to the development of prediction systems that enable accurate age estimation from DNA methylation data. Our objective was to track responses to intense physical exercise of individual age-correlated DNA methylation markers and to infer their potential impact on the aging processes. The study showed accelerated DNA hypermethylation for two CpG sites in TRIM59 and KLF14. Both markers predicted the investigated elite athletes to be several years older than controls and this effect was more substantial in subjects involved in power sports. Accordingly, the complete 5-CpG model revealed age acceleration of elite athletes (P=1.503x10^-7) and the result was more significant amongst power athletes (P=1.051x10^-9). The modified methylation of TRIM59 and KLF14 in top athletes may be accounted for by the biological roles played by these genes. Their known anti-tumour and anti-inflammatory activities suggests that intense physical training has a complex influence on aging and potentially launches signalling networks that contribute to the observed lower risk of elite athletes to develop cardiovascular disease and cancer.

Identifying blood-specific age-related DNA methylation markers on the Illumina MethylationEPIC® BeadChip

The past decade has seen rapid development in DNA methylation (DNAm) microarrays, including the Illumina HumanMethylation27 and HumanMethylation450 (450K) chips, which have played an essential role in identifying and evaluating age-related (AR) DNAm markers in different tissues. Recently, a new array, the Illumina MethylationEPIC (EPIC) was introduced, with nearly double the number of probes as the 450K (∼850,000 probes). In this study, we test these newly added probes for age association using a large cohort of 754 DNAm profiles from blood samples assayed on the EPIC BeadChip, for individuals aged 0-88 years old. 52 AR CpG sites (Spearman's abs(rho) >0.6 and P-value <10^-83) were identified, 21 of which were novel sites and mapped to 18 genes, nine of which (LHFPL4, SLC12A8, EGFEM1P, GPR158, TAL1, KIAA1755, LOC730668, DUSP16, and FAM65C) have never previously been reported to be associated with age. The data were subsequently split into a 527-sample training set and a 227-sample testing set to build and validate two age prediction models using elastic net regression and multivariate regression. Elastic net regression selected 425 CpG markers with a mean absolute deviation (MAD) of 2.6 years based on the testing set. To build a multivariate linear regression model, AR CpG sites with R² > 0.5 at FDR < 0.05 were input into stepwise regression to select the best subset for age prediction. The resulting six CpG markers were linearly modelled with age and explained 81% of age-correlated variation in DNAm levels. Age estimation accuracy using bootstrap analysis was 4.5 years, with 95% confidence intervals of 4.56 to 4.57 years based on the testing set. These results suggest that EPIC BeadChip probes for age estimation fall within the range of probes found on the previous Illumina HumanMethylation platforms in terms of their age-prediction ability.

Age Estimation Based on DNA Methylation Using Blood Samples From Deceased Individuals

Age estimation using DNA methylation levels has been widely investigated in recent years because of its potential application in forensic genetics. The main aim of this study was to develop an age predictor model (APM) for blood samples of deceased individuals based in five age-correlated genes. Fifty-one samples were analyzed through the bisulfite polymerase chain reaction (PCR) sequencing method for DNA methylation evaluation in genes ELOVL2, FHL2, EDARADD, PDE4C, and C1orf132. Linear regression was used to analyze relationships between methylation levels and age. The model using the highest age-correlated CpG from each locus revealed a correlation coefficient of 0.888, explaining 76.3% of age variation, with a mean absolute deviation from the chronological age (MAD) of 6.08 years. The model was validated in an independent test set of 19 samples producing a MAD of 8.84 years. The developed APM seems to be informative and could have potential application in forensic analysis.

Exploring Epigenetic Age in Response to Intensive Relaxing Training: A Pilot Study to Slow Down Biological Age

DNA methylation (DNAm) is an emerging estimator of biological aging, i.e., the often-defined "epigenetic clock", with a unique accuracy for chronological age estimation (DNAmAge). In this pilot longitudinal study, we examine the hypothesis that intensive relaxing training of 60 days in patients after myocardial infarction and in healthy subjects may influence leucocyte DNAmAge by turning back the epigenetic clock. Moreover, we compare DNAmAge with another mechanism of biological age, leucocyte telomere length (LTL) and telomerase. DNAmAge is reduced after training in healthy subjects (p = 0.053), but not in patients. LTL is preserved after intervention in healthy subjects, while it continues to decrease in patients (p = 0.051). The conventional negative correlation between LTL and chronological age becomes positive after training in both patients (p < 0.01) and healthy subjects (p < 0.05). In our subjects, DNAmAge is not associated with LTL. Our findings would suggest that intensive relaxing practices influence different aging molecular mechanisms, i.e., DNAmAge and LTL, with a rejuvenating effect. Our study reveals that DNAmAge may represent an accurate tool to measure the effectiveness of lifestyle-based interventions in the prevention of age-related diseases.

New aspects of dental implants and DNA technology in human identification

Missing, ineligible or delayed reference data to establish conventional dental or DNA identification are common scenarios in forensic practice. Therefore, it is worthwhile to explore new avenues that facilitate human identification. Due to the recent remarkable evolution in the prosthetic dental restorations based on dental implants and the emergence of novel DNA technologies utilized to infer the biological profile, the identification process has become easier than ever before. We report on a characteristic case, which highlights the particular importance of dental implants and DNA approaches in the prospective investigations for human identification. The aim of this publication is to focus on the possibility of identifying the batch numbers, even if they were not engraved in dental implants, making antemortem dental records of dental implants more easily accessible to establish a comparative dental identification. In addition, the reported case presents the supplementary data yielded through estimating the epigenetic age using DNA methylation as well as the biogeographical origin using Y-Haplotype and mitochondrial DNA analyses. Our results demonstrate that expanded oral implant investigations that also include implants extraction and comprehensive microscopic measurements can lead to identifying their batch numbers despite the numerous number of implants systems manufactured and distributed worldwide. Data saved by dental implant manufacturers can be very supportive and represent additional reference data for dental identification, when antemortem dental records are still missing. Furthermore, DNA methylation and mitochondrial DNA analyses can support the progress of investigation.

DNA methylation-based biomarkers and the epigenetic clock theory of ageing

Identifying and validating molecular targets of interventions that extend the human health span and lifespan has been difficult, as most clinical biomarkers are not sufficiently representative of the fundamental mechanisms of ageing to serve as their indicators. In a recent breakthrough, biomarkers of ageing based on DNA methylation data have enabled accurate age estimates for any tissue across the entire life course. These 'epigenetic clocks' link developmental and maintenance processes to biological ageing, giving rise to a unified theory of life course. Epigenetic biomarkers may help to address long-standing questions in many fields, including the central question: why do we age?

Evaluation of six blood-based age prediction models using DNA methylation analysis by pyrosequencing

DNA methylation has been identified as the most promising molecular biomarker for the prediction of age. Several DNA methylation-based models have been proposed for age prediction based on blood samples, using mainly pyrosequencing. These methods present different performances for age prediction and have rarely, if ever, been evaluated and intercompared in an independent validation study. Here, for the first time, we evaluate and compare six blood-based age prediction models (Bekaert¹, Park², Thong³, Weidner⁴, and the Zbiec-Piekarska 1⁵ and Zbiec-Piekarska 2⁶), using DNA methylation analysis by pyrosequencing on 100 blood samples from French individuals aged between 19–65 years. For each model, we perform correlation analysis and evaluate age-prediction performance (mean absolute deviation (MAD) and standard error of the estimate (SEE)). The best age-prediction performances were found with the Bekaert and Thong models (MAD of 4.5–5.2, SEE of 6.8–7.2), followed by the Zbiec-Piekarska 1 model (MAD of 6.8 and SEE of 9.2), while the Park, Weidner and Zbiec-Piekarska 2 models presented lower performances (MAD of 7.2–8.7 and SEE of 9.2–10.3). Given these results, we recommend performing systematic, independent evaluation of all age prediction models on a same cohort to validate the different models and compare their performance.

Studies of hTERT DNA methylation assays on the human age prediction

As an important aspect of epigenetics, DNA methylation has been proven to be suitable for forensic DNA analysis. By detecting changes in DNA methylation, it is desirable to construct a model of age patterns associated with it to infer the age of the individual. The hTERT gene methylation is closely related to tumors, but there are few reports on the relationship between hTERT gene promoter methylation and age. In this study, we utilized the methylation-specific polymerase chain reaction and real-time PCR (relative quantification and absolute quantification) approach to explore the connection between hTERT DNA methylation and age prediction. We fit three models for age prediction based on methylation assay for 90 blood samples from donors aged 1-79 years old. Among them, the model of absolute quantification of real-time enabled the age prediction with R² = 0.9634. We verified the linear regression model with a validation set of 30 blood samples where prediction average error was 4.29 years. Generally, this reliable method improves the DNA methylation analysis of forensic samples.

Human age prediction based on DNA methylation of non-blood tissues

BACKGROUND AND OBJECTIVE

The study of human aging contributes to disease prevention, treatment and life extension. Recently, epigenetics studies have evidenced that there is a close association between DNA methylation and human ages. A quantitatively statistical modeling between DNA methylation and ages could predict the person's age more accurately.

METHODS

We propose a regression model to predict human age based on gradient boosting regressor (GBR). We collect a total of 1280 publicly available non-blood tissues samples with ages ranged from 0 to 90 years old. We calculate the Pearson correlation between CpG's DNA methylation level and age to select age-related CpGs.

RESULTS

Thirteen age-related CpG sites are selected. GBR has the smallest mean absolute deviation to the actual age comparing with other three different models including Bayesian ridge, multiple linear regression, and support vector regression. In the training datasets, the cross-validation results show that the correlation R² between predicted age and DNA methylation is 0.89, and the mean absolute deviation is 4.66 years. In an independent testing set with 262 samples, the GBR achieves the mean absolute deviation of 6.08 years. Meanwhile we also briefly describe the function of the selected thirteen CpG sites.

CONCLUSIONS

We build an age predictor to study the association between ages and the DNA methylation of human non-blood tissues. Our new model provides a more accurate estimation of human ages which will be instrumental for understanding the regulation of DNA methylation on human aging and will accurately monitor the individual aging process.

Validation of methylation-based forensic age estimation in time-series bloodstains on FTA cards and gauze at room temperature conditions

We previously proposed a prediction model consisting of 9 CpG sites for forensic age estimation with high practical potentials in Chinese males. Here, we further evaluated the performance of this prediction model in two independent batches of time-series bloodstain samples naturally exposed to room temperature conditions. The first batch consists of 30 Han Chinese males (18-59 years of age) whose peripheral blood was converted into bloodstains on Flinders Technology Association (FTA) cards and naturally exposed to room temperature conditions for different time points up to 3 months. The second batch consists of 99 Han Chinese males (21-66 years of age) whose peripheral blood was divided into 3 replicates, converted into bloodstains on gauze, and naturally exposed to room temperature conditions for 3 months. For each time point and each replicate, the methylation levels at the 9 CpG sites were detected using the EpiTYPER system. Applying the 9-CpG age prediction model to these bloodstain samples resulted in highly accurate age predictions for all time points and replicates (0.81 <R² < 0.91, 2.94 < MAD < 3.55 years). The updated model combining our previous and current data achieved similarly high prediction results. Therefore, our 9-CpG age prediction model was successfully validated in time-series bloodstain samples converted on both FTA card and gauze under natural room temperature conditions, demonstrating high potentials in future forensic applications to Han Chinese males.

An integrative module analysis of DNA methylation landscape in aging

To investigate the molecular mechanism of aging, the combination of module analysis and DNA methylation data was used to detect dynamically controlled modules for aging. Multiple differential expression networks (DENs) were constructed based on the microarray profiles across different aging groups (<70 years, 70–80 years, and >80 years). Next, a module-based approach was utilized to extract the common candidate modules across all age groups. We used Module Connectivity Dynamic Score (MCDS) to quantify the connectivity change of the common modules among the different age groups. Functional analyses were implemented for the genes in the common modules to further identify the significant biological processes. A total of two DENs were constructed. Overall 657 informative genes were screened out. When false discovery rate (FDR) was set as 0.05, we found that 148 modules were significant. Only 1 significant 2-differential modules (DMs) (module 493) with dynamic changes was discovered. Significantly, the genes in the module 493 participated in 7 significant pathways, including pentose phosphate pathway, carbon metabolism, and citrate cycle (TCA cycle). In conclusion, pathway functions [pentose phosphate pathway, carbon metabolism, citrate cycle (TCA cycle), chromosomal instability, ateroid biosynthesis, PPAR signaling pathway, and immune response] may serve as potential therapeutic targets in aging.

Epigenetic Clock: Just a Convenient Marker or an Active Driver of Aging?

A global DNA hypomethylation and local changes in the methylation levels of specific DNA loci occur during aging in mammals. Global hypomethylation mainly affects highly methylated repeat sequences, such as transposable elements; it is an essentially stochastic process usually referred to as "epigenetic drift." Specific changes in DNA methylation affect various genome sequences and could be either hypomethylation or hypermethylation, but the prevailing tendencies are hypermethylation of promoter sequences associated with CpG islands and hypomethylation of CpG poor genes. Methylation levels of multiple CpG sites display a strong correlation to age common between individuals of the same species. Collectively, methylation of such CpG sites could be used as "epigenetic clocks" to predict biological age. Furthermore, the discrepancy between epigenetic and chronological ages could be predictive of all-cause mortality and multiple age-associated diseases. Random changes in DNA methylation (epigenetic drift) could also affect the aging phenotype, causing accidental changes in gene expression and increasing the transcriptional noise between cells of the same tissue. Both effects could become detrimental to tissue functioning and cause a gradual decline in organ function during aging. Strong evidence shows that epigenetic systems contribute to lifespan control in various organisms. Similar to other cell systems, the epigenome is prone to gradual degradation due to the genome damage, stressful agents and other aging factors. However, unlike mutations and many other hallmarks of aging, age-related epigenetic changes could be fully or partially reversed to a "young" state.

Molecular markers used in forensic genetics

Forensic genetics is a field that has become subject to increasing interest in recent years. Both the technology and the markers used for forensic purposes have changed since the 1980s. The minisatellite sequences used in the famous Pitchfork case introduced genetics to the forensic sciences. Minisatellite sequences have now been replaced by more sensitive microsatellite markers, which have become the basis for the creation of genetic profile databases. Modern molecular methods also exploit single nucleotide polymorphisms, which are often the only way to identify degraded DNA samples. The same type of variation is taken into consideration in attempting to establish the ethnicity of a perpetrator and to determine phenotypic traits such as the eye or hair colour of the individual who is the source of the genetic material. This paper contains a review of the techniques and molecular markers used in human and animal forensic genetics, and also presents the potential trends in forensic genetics such as phenotyping.