New targeted approaches for epigenetic age predictions

pyaging: a Python-based compendium of GPU-optimized aging clocks

MOTIVATION

Aging is intricately linked to diseases and mortality. It is reflected in molecular changes across various tissues which can be leveraged for the development of biomarkers of aging using machine learning models, known as aging clocks. Despite advancements in the field, a significant challenge remains: the lack of robust, Python-based software tools for integrating and comparing these diverse models. This gap highlights the need for comprehensive solutions that can handle the complexity and variety of data in aging research.

RESULTS

To address this gap, I introduce pyaging, a comprehensive open-source Python package designed to facilitate aging research. pyaging harmonizes dozens of aging clocks, covering a range of molecular data types such as DNA methylation, transcriptomics, histone mark ChIP-Seq, and ATAC-Seq. The package is not limited to traditional model types; it features a diverse array, from linear and principal component models to neural networks and automatic relevance determination models. Thanks to a PyTorch-based backend that enables GPU acceleration, pyaging is capable of rapid inference, even when dealing with large datasets and complex models. In addition, the package's support for multi-species analysis extends its utility across various organisms, including humans, various mammals, and Caenorhabditis elegans.

AVAILABILITY AND IMPLEMENTATION

pyaging is accessible on GitHub, at https://github.com/rsinghlab/pyaging, and the distribution is available on PyPi, at https://pypi.org/project/pyaging/. The software is also archived on Zenodo, at https://zenodo.org/doi/10.5281/zenodo.10335011.

A high-throughput droplet digital PCR system aiming eight DNA methylation targets for age prediction

The droplet digital Polymerase Chain Reaction (ddPCR) has garnered recognition for its distinctive attribute of absolute quantification. And it has found practical utility in age prediction through DNA methylation profiles. However, a prevalent limitation in current ddPCR methodologies is the restricted capacity to detect only two targets concurrently in most instruments, leading to high costs, sample wastage, and labor-intensive procedures. To address the limitations, a novel high-throughput ddPCR system allowing for the simultaneous detection of eight targets was developed. Through the implementation of a new 8-plex ddPCR assay, coupled with comprehensive linear regression analyses involving primers and probes ratios, diverse inputs of single CpG sites with distinct primers and probes, and varying plex assay configurations, stable DNA methylation values for four CpGs and stable measurement precisions for distinct multiplex systems were consistently observed. These findings pave the way for advancing the field of chemistry science by enabling more efficient and cost-effective methods. Furthermore, the comparative validation of ddPCR and SNaPshot demonstrated a remarkable concordance in results, and the system also displayed well in the field of various aspects, including species specificity, DNA input, and aged samples. In this study, the recommended input of bisulfite-converted DNA was determined to be 10-50 ng due to the double-positive droplets. Notably, the Pearson correlation coefficient squared values of four CpGs were 0.4878 (ASPA), 0.4832 (IGSF1), 0.6881 (COL1A1), and 0.6475 (MEIS1-AS3). And the testing set exhibited a mean absolute error of 4.5923 years, indicating the robustness and accuracy of the age-predictive model.

Predicting age from blood by droplet digital PCR using a set of three DNA methylation markers

Evaluation of DNA methylation (DNAm) patterns is a promising tool for age estimation. The duplex droplet digital PCR (ddPCR) method has been recently investigated for DNAm evaluation, revealing to be a potential methodology for DNAm evaluation and molecular age estimation. In this study, we evaluated DNAm levels of CpGs located at the three age-associated genes ELOVL2, FHL2 and PDE4C using ddPCR to develop an age prediction model. Blood-derived DNA samples from 58 healthy individuals (42 women and 16 men; aged 1-93 years old) were submitted to bisulfite conversion followed by ddPCR using dual-labeled probes targeting methylated and unmethylated DNA sequences. Simple linear regression statistics revealed a strong correlation between DNAm levels and chronological age for FHL2 (R = 0.948; P = 1.472 × 10-29) and PDE4C (R = 0.819; P = 3.917 × 10-15), addressing only one CpG for each gene. For the ELOVL2 gene, evaluating five CpG sites in simultaneous, revealed a strong age correlation (R = 0.887; P = 2.099 × 10-20) in a simple linear regression statistics and very strong age correlation (R = 0.926; P = 2.202 × 10-25) when using quadratic regression statistics. The multivariable regression analysis, using methylation information captured on ELOVL2 (squared), FHL2 and PDE4C genes, revealed a very strong age correlation (R = 0.970; P = 5.356 ×10-33), explaining 93.7 % of age variance, displaying a mean absolute deviation (MAD) between chronological and predicted age of 4.657 years (RMSE = 6.044). We postulate that the ddPCR method should be further investigated for DNAm-based age prediction, because it is a relatively simple and an accurate method that can be routinely used in forensic laboratories for testing a few numbers of markers.

Epigenetic studies in children at risk of stunting and their parents in India, Indonesia and Senegal: a UKRI GCRF Action Against Stunting Hub protocol paper

INTRODUCTION

In 2020, an estimated 150 million children under the age of 5 years were stunted. Stunting results from early-life adversity and it is associated with significant physical and cognitive deficit, lifelong socioeconomic disadvantage and reduced life expectancy. There is a need to understand the causes of stunting and its effects in order to develop strategies to avoid it and to mitigate the consequences once stunting has occurred. Epigenetics is an important mechanism through which early-life factors are thought to influence biological function, with long-term consequences. We describe a series of epigenetic studies designed to understand how early-life adversity results in stunting and to inform the development of practical tools such as predictive markers and therapeutic targets. This work is part of the UKRI GCRF Action Against Stunting Hub.

METHODS AND ANALYSIS

The project-in India, Indonesia and Senegal-comprises an observational study of mothers, fathers, and offspring (n=500) spanning the first 1000 days of life, and an intervention study in each country. Epigenetic status (DNA methylation) is determined in saliva from babies collected within 1 month of birth and again at 18 months of age, and from mothers and fathers around the time of birth. Epigenome-wide analysis is carried out using the Illumina EPIC array, augmented by high-definition sequencing approaches. Statistical analysis is carried out at the level of candidate genes/regions, higher dimensional epigenetic states and epigenome-wide association. Data analysis focuses on the determinants of stunting, the effectiveness of interventions, population comparisons and the link between epigenetics and other thematic areas, which include anthropometry, microbiome, gut health, parasitology, cognition, nutrition, food hygiene and water sanitation, food systems and the home environment.

ETHICS AND DISSEMINATION

This study has been approved by the relevant Ethics Committees in Indonesia, India and Senegal, and the UK. Research data will be published and posted in public repositories.

TIME-seq reduces time and cost of DNA methylation measurement for epigenetic clock construction

Epigenetic 'clocks' based on DNA methylation have emerged as the most robust and widely used aging biomarkers, but conventional methods for applying them are expensive and laborious. Here we develop tagmentation-based indexing for methylation sequencing (TIME-seq), a highly multiplexed and scalable method for low-cost epigenetic clocks. Using TIME-seq, we applied multi-tissue and tissue-specific epigenetic clocks in over 1,800 mouse DNA samples from eight tissue and cell types. We show that TIME-seq clocks are accurate and robust, enriched for polycomb repressive complex 2-regulated loci, and benchmark favorably against conventional methods despite being up to 100-fold less expensive. Using dietary treatments and gene therapy, we find that TIME-seq clocks reflect diverse interventions in multiple tissues. Finally, we develop an economical human blood clock (R > 0.96, median error = 3.39 years) in 1,056 demographically representative individuals. These methods will enable more efficient epigenetic clock measurement in larger-scale human and animal studies.

Epigenetic drift underlies epigenetic clock signals, but displays distinct responses to lifespan interventions, development, and cellular dedifferentiation

Changes in DNA methylation with age are observed across the tree of life. The stereotypical nature of these changes can be modeled to produce epigenetic clocks capable of predicting chronological age with unprecedented accuracy. Despite the predictive ability of epigenetic clocks and their utility as biomarkers in clinical applications, the underlying processes that produce clock signals are not fully resolved, which limits their interpretability. Here, we develop a computational approach to spatially resolve the within read variability or "disorder" in DNA methylation patterns and test if age-associated changes in DNA methylation disorder underlie signals comprising epigenetic clocks. We find that epigenetic clock loci are enriched in regions that both accumulate and lose disorder with age, suggesting a link between DNA methylation disorder and epigenetic clocks. We then develop epigenetic clocks that are based on regional disorder of DNA methylation patterns and compare their performance to other epigenetic clocks by investigating the influences of development, lifespan interventions, and cellular dedifferentiation. We identify common responses as well as critical differences between canonical epigenetic clocks and those based on regional disorder, demonstrating a fundamental decoupling of epigenetic aging processes. Collectively, we identify key linkages between epigenetic disorder and epigenetic clocks and demonstrate the multifaceted nature of epigenetic aging in which stochastic processes occurring at non-random loci produce predictable outcomes.

First-time application of droplet digital PCR for methylation testing of the 11p15.5 imprinting regions

BACKGROUND

Beckwith-Wiedemann syndrome and Silver-Russel syndrome are two imprinting disorders caused by opposite molecular alterations in 11p15.5. With the current diagnostic tests, their molecular diagnosis is challenging due to molecular heterogeneity and mosaic occurrence of the most frequent alterations. As the determination of precise (epi)genotype of patients is relevant as the basis for a personalized treatment, different approaches are needed to increase the sensitivity of diagnostic testing of imprinting disorders.

METHODS

We established methylation-specific droplet digital PCR approaches (MS-ddPCR) for the two imprinting centers in 11p15.5, and analyzed patients with paternal uniparental disomy of chromosome 11p15.5 (upd(11)pat) and other imprinting defects in the region. The results were compared to those from MS-MLPA (multiplex ligation-dependent probe amplification) and MS-pyrosequencing.

RESULTS

MS-ddPCR confirmed the molecular alterations in all patients and the results matched well with MS-MLPA. The results of MS-pyrosequencing varied between different runs, whereas MS-ddPCR results were reproducible.

CONCLUSION

We show for the first time that MS-ddPCR is a reliable and easy applicable method for determination of MS-associated changes in imprinting disorders. It is therefore an additional tool for multimethod diagnostics of imprinting disorders suitable to improve the diagnostic yield.

CTCF deletion alters the pluripotency and DNA methylation profile of human iPSCs

Pluripotent stem cells are characterized by their differentiation potential toward endoderm, mesoderm, and ectoderm. However, it is still largely unclear how these cell-fate decisions are mediated by epigenetic mechanisms. In this study, we explored the relevance of CCCTC-binding factor (CTCF), a zinc finger-containing DNA-binding protein, which mediates long-range chromatin organization, for directed cell-fate determination. We generated human induced pluripotent stem cell (iPSC) lines with deletions in the protein-coding region in exon 3 of CTCF, resulting in shorter transcripts and overall reduced protein expression. Chromatin immunoprecipitation showed a considerable loss of CTCF binding to target sites. The CTCF deletions resulted in slower growth and modest global changes in gene expression, with downregulation of a subset of pluripotency-associated genes and neuroectodermal genes. CTCF deletion also evoked DNA methylation changes, which were moderately associated with differential gene expression. Notably, CTCF-deletions lead to upregulation of endo-mesodermal associated marker genes and epigenetic signatures, whereas ectodermal differentiation was defective. These results indicate that CTCF plays an important role in the maintenance of pluripotency and differentiation, especially towards ectodermal lineages.

Targeted DNA Methylation Analysis Facilitates Leukocyte Counts in Dried Blood Samples

BACKGROUND

Cell-type specific DNA methylation (DNAm) can be employed to determine the numbers of leukocyte subsets in blood. In contrast to conventional methods for leukocyte counts, which are based on cellular morphology or surface marker protein expression, the cellular deconvolution based on DNAm levels is applicable for frozen or dried blood. Here, we further enhanced targeted DNAm assays for leukocyte counts in clinical application.

METHODS

DNAm profiles of 40 different studies were compiled to identify CG dinucleotides (CpGs) with cell-type specific DNAm using a computational framework, CimpleG. DNAm levels at these CpGs were then measured with digital droplet PCR in venous blood from 160 healthy donors and 150 patients with various hematological disorders. Deconvolution was further validated with venous blood (n = 75) and capillary blood (n = 31) that was dried on Whatman paper or on Mitra microsampling devices.

RESULTS

In venous blood, automated cell counting or flow cytometry correlated well with epigenetic estimates of relative leukocyte counts for granulocytes (r = 0.95), lymphocytes (r = 0.97), monocytes (r = 0.82), CD4 T cells (r = 0.84), CD8 T cells (r = 0.94), B cells (r = 0.96), and NK cells (r = 0.72). Similar correlations and precisions were achieved for dried blood samples. Spike-in with a reference plasmid enabled accurate epigenetic estimation of absolute leukocyte counts from dried blood samples, correlating with conventional venous (r = 0.86) and capillary (r = 0.80) blood measurements.

CONCLUSIONS

The advanced selection of cell-type specific CpGs and utilization of digital droplet PCR analysis provided accurate epigenetic blood counts. Analysis of dried blood facilitates self-sampling with a finger prick, thereby enabling easier accessibility to testing.

Getting the chronological age out of DNA: using insights of age-dependent DNA methylation for forensic DNA applications

BACKGROUND

DNA analysis for forensic investigations has a long tradition with important developments and optimizations since its first application. Traditionally, short tandem repeats analysis has been the most powerful method for the identification of individuals. However, in addition, epigenetic changes, i.e., DNA methylation, came into focus of forensic DNA research. Chronological age prediction is one promising application to allow for narrowing the pool of possible individuals who caused a trace, as well as to support the identification of unknown bodies and for age verification of living individuals.

OBJECTIVE

This review aims to provide an overview of the current knowledge, possibilities, and (current) limitations about DNA methylation-based chronological age prediction with emphasis on forensic application.

METHODS

The development, implementation and application of age prediction tools requires a deep understanding about the biological background, the analysis methods, the age-dependent DNA methylation markers, as well as the mathematical models for age prediction and their evaluation. Furthermore, additional influences can have an impact. Therefore, the literature was evaluated in respect to these diverse topics.

CONCLUSION

The numerous research efforts in recent years have led to a rapid change in our understanding of the application of DNA methylation for chronological age prediction, which is now on the way to implementation and validation. Knowledge of the various aspects leads to a better understanding and allows a more informed interpretation of DNAm quantification results, as well as the obtained results by the age prediction tools.

Accurate age prediction from blood using a small set of DNA methylation sites and a cohort-based machine learning algorithm

Chronological age prediction from DNA methylation sheds light on human aging, health, and lifespan. Current clocks are mostly based on linear models and rely upon hundreds of sites across the genome. Here, we present GP-age, an epigenetic non-linear cohort-based clock for blood, based upon 11,910 methylomes. Using 30 CpG sites alone, GP-age outperforms state-of-the-art models, with a median accuracy of ∼2 years on held-out blood samples, for both array and sequencing-based data. We show that aging-related changes occur at multiple neighboring CpGs, with implications for using fragment-level analysis of sequencing data in aging research. By training three independent clocks, we show enrichment of donors with consistent deviation between predicted and actual age, suggesting individual rates of biological aging. Overall, we provide a compact yet accurate alternative to array-based clocks for blood, with applications in longitudinal aging research, forensic profiling, and monitoring epigenetic processes in transplantation medicine and cancer.

Culture expansion of CAR T cells results in aberrant DNA methylation that is associated with adverse clinical outcome

Chimeric antigen receptor (CAR) T cells provide new perspectives for treatment of hematological malignancies. Manufacturing of these cellular products includes culture expansion procedures, which may affect cellular integrity and therapeutic outcome. In this study, we investigated culture-associated epigenetic changes in CAR T cells and found continuous gain of DNAm, particularly within genes that are relevant for T cell function. Hypermethylation in many genes, such as TCF7, RUNX1, and TOX, was reflected by transcriptional downregulation. 332 CG dinucleotides (CpGs) showed an almost linear gain in methylation with cell culture time, albeit neighboring CpGs were not coherently regulated on the same DNA strands. An epigenetic signature based on 14 of these culture-associated CpGs predicted cell culture time across various culture conditions. Notably, even in CAR T cell products of similar culture time higher DNAm levels at these CpGs were associated with significantly reduced long-term survival post transfusion. Our data demonstrate that cell culture expansion of CAR T cells evokes DNA hypermethylation at specific sites in the genome and the signature may also reflect loss of potential in CAR T cell products. Hence, reduced cultivation periods are beneficial to avoid dysfunctional methylation programs that seem to be associated with worse therapeutic outcome.

Introduction of a multiplex amplicon sequencing assay to quantify DNA methylation in target cytosine markers underlying four selected epigenetic clocks

BACKGROUND

DNA methylation analysis has proven to be a powerful tool for age assessment. However, the implementation of epigenetic age prediction in diagnostics or routine forensic casework requires appropriate laboratory methods. In this study, we aimed to compare the performance of large-scale DNA methylation analysis protocols that show promise in terms of accuracy, throughput, multiplexing capacity, and high sensitivity.

RESULTS

The protocols were designed to target a predefined panel of 161 genomic CG/CA sites from four known estimators of epigenetic age-related parameters, optimized and validated using artificially methylated controls or blood samples. We successfully targeted 96% of these loci using two enrichment protocols: Ion AmpliSeq™, an amplicon-based method integrated with Ion Torrent S5, and SureSelectXT Methyl-Seq, a hybridization-based method followed by MiSeq FGx sequencing. Both protocols demonstrated high accuracy and robustness. Although hybridization assays have greater multiplexing capabilities, the best overall performance was observed for the amplicon-based protocol with the lowest variability in DNA methylation at 25 ng of starting DNA, mean observed marker coverage of ~ 6.7 k reads, and accuracy of methylation quantification with a mean absolute difference between observed and expected methylation beta value of 0.054. The Ion AmpliSeq method correlated strongly with genome-scale EPIC microarray data (R = 0.91) and showed superiority in terms of methylation measurement accuracy. Method-to-method bias was accounted for by the use of linear transformation, which provided a highly accurate prediction of calendar age with a mean absolute error of less than 5 years for the VISAGE and Hannum age clocks used. The pace of aging (PoAm) and the mortality risk score (MRS) estimators included in our panel represent next-generation clocks, were found to have low to moderate correlations with the VISAGE and Hannum models (R < 0.75), and thus may capture different aspects of epigenetic aging.

CONCLUSIONS

We propose a laboratory tool that allows the quantification of DNA methylation in cytosines underlying four different clocks, thus providing broad information on epigenetic aging while maintaining a reasonable number of CpG markers, opening the way to a wide range of applications in forensics, medicine, and healthcare.

Role of primary aging hallmarks in Alzheimer´s disease

Alzheimer's disease (AD) is the most common neurodegenerative disease, which severely threatens the health of the elderly and causes significant economic and social burdens. The causes of AD are complex and include heritable but mostly aging-related factors. The primary aging hallmarks include genomic instability, telomere wear, epigenetic changes, and loss of protein stability, which play a dominant role in the aging process. Although AD is closely associated with the aging process, the underlying mechanisms involved in AD pathogenesis have not been well characterized. This review summarizes the available literature about primary aging hallmarks and their roles in AD pathogenesis. By analyzing published literature, we attempted to uncover the possible mechanisms of aberrant epigenetic markers with related enzymes, transcription factors, and loss of proteostasis in AD. In particular, the importance of oxidative stress-induced DNA methylation and DNA methylation-directed histone modifications and proteostasis are highlighted. A molecular network of gene regulatory elements that undergoes a dynamic change with age may underlie age-dependent AD pathogenesis, and can be used as a new drug target to treat AD.

A Targeted Epigenetic Clock for the Prediction of Biological Age

Epigenetic clocks were initially developed to track chronological age, but accumulating evidence indicates that they can also predict biological age. They are usually based on the analysis of DNA methylation by genome-wide methods, but targeted approaches, based on the assessment of a small number of CpG sites, are advisable in several settings. In this study, we developed a targeted epigenetic clock purposely optimized for the measurement of biological age. The clock includes six genomic regions mapping in ELOVL2, NHLRC1, AIM2, EDARADD, SIRT7 and TFAP2E genes, selected from a re-analysis of existing microarray data, whose DNA methylation is measured by EpiTYPER assay. In healthy subjects (n = 278), epigenetic age calculated using the targeted clock was highly correlated with chronological age (Spearman correlation = 0.89). Most importantly, and in agreement with previous results from genome-wide clocks, epigenetic age was significantly higher and lower than expected in models of increased (persons with Down syndrome, n = 62) and decreased (centenarians, n = 106; centenarians' offspring, n = 143; nutritional intervention in elderly, n = 233) biological age, respectively. These results support the potential of our targeted epigenetic clock as a new marker of biological age and open its evaluation in large cohorts to further promote the assessment of biological age in healthcare practice.

Epigenetic biomarkers to track differentiation of pluripotent stem cells

Quality control of induced pluripotent stem cells remains a challenge. For validation of the pluripotent state, it is crucial to determine trilineage differentiation potential toward endoderm, mesoderm, and ectoderm. Here, we report GermLayerTracker, a combination of site-specific DNA methylation (DNAm) assays that serve as biomarker for early germ layer specification. CG dinucleotides (CpGs) were identified with characteristic DNAm at pluripotent state and after differentiation into endoderm, mesoderm, and ectoderm. Based on this, a pluripotency score was derived that tracks reprogramming and may indicate differentiation capacity, as well as lineage-specific scores to monitor either directed differentiation or self-organized multilineage differentiation in embryoid bodies. Furthermore, we established pyrosequencing assays for fast and cost-effective analysis. In the future, the GermLayerTracker could be used for quality control of pluripotent cells and to estimate lineage-specific commitment during initial differentiation events.

A pan-tissue DNA-methylation epigenetic clock based on deep learning

Several age predictors based on DNA methylation, dubbed epigenetic clocks, have been created in recent years, with the vast majority based on regularized linear regression. This study explores the improvement in the performance and interpretation of epigenetic clocks using deep learning. First, we gathered 142 publicly available data sets from several human tissues to develop AltumAge, a neural network framework that is a highly accurate and precise age predictor. Compared to ElasticNet, AltumAge performs better for within-data set and cross-data set age prediction, being particularly more generalizable in older ages and new tissue types. We then used deep learning interpretation methods to learn which methylation sites contributed to the final model predictions. We observe that while most important CpG sites are linearly related to age, some highly-interacting CpG sites can influence the relevance of such relationships. Using chromatin annotations, we show that the CpG sites with the highest contribution to the model predictions were related to gene regulatory regions in the genome, including proximity to CTCF binding sites. We also found age-related KEGG pathways for genes containing these CpG sites. Lastly, we performed downstream analyses of AltumAge to explore its applicability and compare its age acceleration with Horvath’s 2013 model. We show that our neural network approach predicts higher age acceleration for tumors, for cells that exhibit age-related changes in vitro, such as immune and mitochondrial dysfunction, and for samples from patients with multiple sclerosis, type 2 diabetes, and HIV, among other conditions. Altogether, our neural network approach provides significant improvement and flexibility compared to current epigenetic clocks for both performance and model interpretability.

Biological Age Predictors: The Status Quo and Future Trends

There is no single universal biomarker yet to estimate overall health status and longevity prospects. Moreover, a consensual approach to the very concept of aging and the means of its assessment are yet to be developed. Markers of aging could facilitate effective health control, more accurate life expectancy estimates, and improved health and quality of life. Clinicians routinely use several indicators that could be biomarkers of aging. Duly validated in a large cohort, models based on a combination of these markers could provide a highly accurate assessment of biological age and the pace of aging. Biological aging is a complex characteristic of chronological age (usually), health-to-age concordance, and medically estimated life expectancy. This study is a review of the most promising techniques that could soon be used in routine clinical practice. Two main selection criteria were applied: a sufficient sample size and reliability based on validation. The selected biological age calculators were grouped according to the type of biomarker used: (1) standard clinical and laboratory markers; (2) molecular markers; and (3) epigenetic markers. The most accurate were the calculators, which factored in a variety of biomarkers. Despite their demonstrated effectiveness, most of them require further improvement and cannot yet be considered for use in standard clinical practice. To illustrate their clinical application, we reviewed their use during the COVID-19 pandemic.

A set of common buccal CpGs that predict epigenetic age and associate with lifespan-regulating genes

Epigenetic aging clocks are computational models that use DNA methylation sites to predict age. Since cheek swabs are non-invasive and painless, collecting DNA from buccal tissue is highly desirable. Here, we review 11 existing clocks that have been applied to buccal tissue. Two of these were exclusively trained on adults and, while moderately accurate, have not been used to capture health-relevant differences in epigenetic age. Using 130 common CpGs utilized by two or more existing buccal clocks, we generate a proof-of-concept predictor in an adult methylomic dataset. In addition to accurately estimating age (r = 0.95 and mean absolute error = 3.88 years), this clock predicted that Down syndrome subjects were significantly older relative to controls. A literature and database review of CpG-associated genes identified numerous genes (e.g., CLOCK, ELOVL2, and VGF) and molecules (e.g., alpha-linolenic acid, glycine, and spermidine) reported to influence lifespan and/or age-related disease in model organisms.

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130 CpGs have been used by two or more aging clocks applied to human buccal tissue

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Common CpG genes are linked to the adaptive immune system and telomere maintenance

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Common CpGs can be used to build a novel, proof-of-concept epigenetic aging clock

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Several compounds associated with common CpG genes regulate lifespan in animals

A cautionary note on altered pace of aging in the COVID-19 era

Coronavirus disease 2019 (COVID-19) is highly age-dependent due to hi-jacking the molecular control of the immune cells by the severe acute respiratory syndrome-corona virus 2 (SARS-CoV-2) leading to aberrant DNA methylation (DNAm) pattern of blood in comparison to normal individuals. These epigenetic modifications have been linked to perturbations to the epigenetic clock, development of long COVID-19 syndrome, and all-cause mortality risk. I reviewed the effects of COVID-19 on different molecular age markers such as the DNAm, telomere length (TL), and signal joint T-cell receptor excision circle (sjTREC). Integrating the accumulated clinical research data, COVID-19 and novel medical management may alter the pace of aging in adult individuals (<60 years). As such, COVID-19 might be a confounder in epigenetic age estimation similar to life style diversities, pathogens and pathologies which may influence the interpretation of DNAm data. Similarly, the SARS-CoV-2 affects T-lymphocyte function with possible influence on sjTREC levels. In contrast, TL measurements performed years before the SARS-CoV-2 pandemic proved that short TL predisposes to severe COVID- 19 independently from chronological age. However, the persistence of COVID-19 epigenetic scars and the durability of the immune response after vaccination and their effect on the ongoing pace of aging are still unknown. In the light of these data, the heterogeneous nature of the samples in these studies mandates a systematic evaluation of the currrent methods. SARS-CoV-2 may modify the reliability of the age estimation models in real casework because blood is the most common biological sample encountered in forensic contexts.

Altered DNA methylation at age-associated CpG sites in children with growth disorders: impact on age estimation?

Age estimation based on DNA methylation (DNAm) can be applied to children, adolescents and adults, but many CG dinucleotides (CpGs) exhibit different kinetics of age-associated DNAm across these age ranges. Furthermore, it is still unclear how growth disorders impact epigenetic age predictions, and this may be particularly relevant for a forensic application. In this study, we analyzed buccal mucosa samples from 95 healthy children and 104 children with different growth disorders. DNAm was analysed by pyrosequencing for 22 CpGs in the genes PDE4C, ELOVL2, RPA2, EDARADD and DDO. The relationship between DNAm and age in healthy children was tested by Spearman’s rank correlation. Differences in DNAm between the groups “healthy children” and the (sub-)groups of children with growth disorders were tested by ANCOVA. Models for age estimation were trained (1) based on the data from 11 CpGs with a close correlation between DNAm and age (R ≥ 0.75) and (2) on five CpGs that also did not present significant differences in DNAm between healthy and diseased children. Statistical analysis revealed significant differences between the healthy group and the group with growth disorders (11 CpGs), the subgroup with a short stature (12 CpGs) and the non-short stature subgroup (three CpGs). The results are in line with the assumption of an epigenetic regulation of height-influencing genes. Age predictors trained on 11 CpGs with high correlations between DNAm and age revealed higher mean absolute errors (MAEs) in the group of growth disorders (mean MAE 2.21 years versus MAE 1.79 in the healthy group) as well as in the short stature (sub-)groups; furthermore, there was a clear tendency for overestimation of ages in all growth disorder groups (mean age deviations: total growth disorder group 1.85 years, short stature group 1.99 years). Age estimates on samples from children with growth disorders were more precise when using a model containing only the five CpGs that did not present significant differences in DNAm between healthy and diseased children (mean age deviations: total growth disorder group 1.45 years, short stature group 1.66 years). The results suggest that CpGs in genes involved in processes relevant for growth and development should be avoided in age prediction models for children since they may be sensitive for alterations in the DNAm pattern in cases of growth disorders.

How to Translate DNA Methylation Biomarkers Into Clinical Practice

Recent advances in sequencing technologies provide unprecedented opportunities for epigenetic biomarker development. Particularly the DNA methylation pattern—which is modified at specific sites in the genome during cellular differentiation, aging, and disease—holds high hopes for a wide variety of diagnostic applications. While many epigenetic biomarkers have been described, only very few of them have so far been successfully translated into clinical practice and almost exclusively in the field of oncology. This discrepancy might be attributed to the different demands of either publishing a new finding or establishing a standardized and approved diagnostic procedure. This is exemplified for epigenetic leukocyte counts and epigenetic age-predictions. To ease later clinical translation, the following hallmarks should already be taken into consideration when designing epigenetic biomarkers: 1) Identification of best genomic regions, 2) pre-analytical processing, 3) accuracy of DNA methylation measurements, 4) identification of confounding parameters, 5) accreditation as diagnostic procedure, 6) standardized data analysis, 7) turnaround time, and 8) costs and customer requirements. While the initial selection of relevant genomic regions is usually performed on genome wide DNA methylation profiles, it might be advantageous to subsequently establish targeted assays that focus on specific genomic regions. Development of an epigenetic biomarker for clinical application is a long and cumbersome process that is only initiated with the identification of an epigenetic signature.

A mammalian methylation array for profiling methylation levels at conserved sequences

Infinium methylation arrays are not available for the vast majority of non-human mammals. Moreover, even if species-specific arrays were available, probe differences between them would confound cross-species comparisons. To address these challenges, we developed the mammalian methylation array, a single custom array that measures up to 36k CpGs per species that are well conserved across many mammalian species. We designed a set of probes that can tolerate specific cross-species mutations. We annotate the array in over 200 species and report CpG island status and chromatin states in select species. Calibration experiments demonstrate the high fidelity in humans, rats, and mice. The mammalian methylation array has several strengths: it applies to all mammalian species even those that have not yet been sequenced, it provides deep coverage of conserved cytosines facilitating the development of epigenetic biomarkers, and it increases the probability that biological insights gained in one species will translate to others.

Methods to probe DNA methylation in the majority of non-human mammals are lacking. Here the authors developed a Mammalian Methylation Array that includes 36k well-conserved CpGs in mammals which will facilitate cross-species comparisons. They annotate the conserved CpGs in > 200 species. The array allows one to measure methylation in all mammalian species including unsequenced ones.

Unique Associations of DNA Methylation Regions With 24-Hour Blood Pressure Phenotypes in Blacks

BACKGROUND

Epigenetic marks (eg, DNA methylation) may capture the effect of gene-environment interactions. DNA methylation is involved in blood pressure (BP) regulation and hypertension development; however, no studies have evaluated its relationship with 24-hour BP phenotypes (daytime, nighttime, and 24-hour average BPs).

METHODS

We examined the association of whole blood DNA methylation with 24-hour BP phenotypes and clinic BPs in a discovery cohort of 281 Blacks using reduced representation bisulfite sequencing. We developed a deep and region-specific methylation sequencing method, Bisulfite ULtrapLEx Targeted Sequencing and utilized it to validate our findings in a separate validation cohort (n=117).

RESULTS

Analysis of 38 215 DNA methylation regions (MRs), derived from 1 549 368 CpG sites across the genome, identified up to 72 regions that were significantly associated with 24-hour BP phenotypes. No MR was significantly associated with clinic BP. Two to 3 MRs were significantly associated with various 24-hour BP phenotypes after adjustment for age, sex, and body mass index. Together, these MRs explained up to 16.5% of the variance of 24-hour average BP, while age, sex, and BMI explained up to 11.0% of the variance. Analysis of one of the MRs in an independent cohort using Bisulfite ULtrapLEx Targeted Sequencing confirmed its association with 24-hour average BP phenotype.

CONCLUSIONS

We identified several MRs that explain a substantial portion of variances in 24-hour BP phenotypes, which might be excellent markers of cumulative effect of factors influencing 24-hour BP levels. The Bisulfite ULtrapLEx Targeted Sequencing workflow has potential to be suitable for clinical testing and population screenings on a large scale.

Profiling epigenetic age in single cells

DNA methylation dynamics emerged as a promising biomarker of mammalian aging, with multivariate machine learning models ('epigenetic clocks') enabling measurement of biological age in bulk tissue samples. However, intrinsically sparse and binarized methylation profiles of individual cells have so far precluded the assessment of aging in single-cell data. Here, we introduce scAge, a statistical framework for epigenetic age profiling at single-cell resolution, and validate our approach in mice. Our method recapitulates the chronological age of tissues, while uncovering heterogeneity among cells. We show accurate tracking of the aging process in hepatocytes, demonstrate attenuated epigenetic aging in muscle stem cells, and track age dynamics in embryonic stem cells. We also use scAge to reveal, at the single-cell level, a natural and stratified rejuvenation event occurring during early embryogenesis. We provide our framework as a resource to enable exploration of epigenetic aging trajectories at single-cell resolution.

Analysis of 14C, 13C and Aspartic Acid Racemization in Teeth and Bones to Facilitate Identification of Unknown Human Remains: Outcomes of Practical Casework

The identification of unknown human remains represents an important task in forensic casework. If there are no clues as to the identity of the remains, then the age, sex, and origin are the most important factors to limit the search for a matching person. Here, we present the outcome of application of so-called bomb pulse radiocarbon (14C derived from above-ground nuclear bomb tests during 1955-1963) analysis to birthdate human remains. In nine identified cases, 14C analysis of tooth crowns provided an estimate of the true date of birth with an average absolute error of 1.2 ± 0.8 years. Analysis of 14C in tooth roots also showed a good precision with an average absolute error of 2.3 ± 2.5 years. Levels of 14C in bones can determine whether a subject has lived after 1955 or not, but more precise carbon turnover data for bones would be needed to calculate date of birth and date of death. Aspartic acid racemization analysis was performed on samples from four cases; in one of these, the year of birth could be predicted with good precision, whereas the other three cases are still unidentified. The stable isotope 13C was analyzed in tooth crowns to estimate provenance. Levels of 13C indicative of Scandinavian provenance were found in known Scandinavian subjects. Teeth from four Polish subjects all showed higher 13C levels than the average for Scandinavian subjects.

Evidence for differences in DNA methylation between Germans and Japanese

As a contribution to the discussion about the possible effects of ethnicity/ancestry on age estimation based on DNA methylation (DNAm) patterns, we directly compared age-associated DNAm in German and Japanese donors in one laboratory under identical conditions. DNAm was analyzed by pyrosequencing for 22 CpG sites (CpGs) in the genes PDE4C, RPA2, ELOVL2, DDO, and EDARADD in buccal mucosa samples from German and Japanese donors (N = 368 and N = 89, respectively).

Twenty of these CpGs revealed a very high correlation with age and were subsequently tested for differences between German and Japanese donors aged between 10 and 65 years (N = 287 and N = 83, respectively). ANCOVA was performed by testing the Japanese samples against age- and sex-matched German subsamples (N = 83 each; extracted 500 times from the German total sample). The median p values suggest a strong evidence for significant differences (p < 0.05) at least for two CpGs (EDARADD, CpG 2, and PDE4C, CpG 2) and no differences for 11 CpGs (p > 0.3).

Age prediction models based on DNAm data from all 20 CpGs from German training data did not reveal relevant differences between the Japanese test samples and German subsamples. Obviously, the high number of included “robust CpGs” prevented relevant effects of differences in DNAm at two CpGs.

Nevertheless, the presented data demonstrates the need for further research regarding the impact of confounding factors on DNAm in the context of ethnicity/ancestry to ensure a high quality of age estimation. One approach may be the search for “robust” CpG markers—which requires the targeted investigation of different populations, at best by collaborative research with coordinated research strategies.

Epigenetic Clocks Are Not Accelerated in COVID-19 Patients

Age is a major risk factor for severe outcome of the 2019 coronavirus disease (COVID-19). In this study, we followed the hypothesis that particularly patients with accelerated epigenetic age are affected by severe outcomes of COVID-19. We investigated various DNA methylation datasets of blood samples with epigenetic aging signatures and performed targeted bisulfite amplicon sequencing. Overall, epigenetic clocks closely correlated with the chronological age of patients, either with or without acute respiratory distress syndrome. Furthermore, lymphocytes did not reveal significantly accelerated telomere attrition. Thus, these biomarkers cannot reliably predict higher risk for severe COVID-19 infection in elderly patients.

DNA methylation changes during long-term in vitro cell culture are caused by epigenetic drift

Culture expansion of primary cells evokes highly reproducible DNA methylation (DNAm) changes. We have identified CG dinucleotides (CpGs) that become continuously hyper- or hypomethylated during long-term culture of mesenchymal stem cells (MSCs) and other cell types. Bisulfite barcoded amplicon sequencing (BBA-seq) demonstrated that DNAm patterns of neighboring CpGs become more complex without evidence of continuous pattern development and without association to oligoclonal subpopulations. Circularized chromatin conformation capture (4C) revealed reproducible changes in nuclear organization between early and late passages, while there was no enriched interaction with other genomic regions that also harbor culture-associated DNAm changes. Chromatin immunoprecipitation of CTCF did not show significant differences during long-term culture of MSCs, however culture-associated hypermethylation was enriched at CTCF binding sites and hypomethylated CpGs were devoid of CTCF. Taken together, our results support the notion that DNAm changes during culture-expansion are not directly regulated by a targeted mechanism but rather resemble epigenetic drift.

Julia Franzen et al. investigate if changes in DNA methylation at specific genetic loci during cell culture expansion are due to a specific mechanism or gradual deregulation of an epigenetic state. Their results suggest that changes in CpG methylation are due to indirect epigenetic drift, rather than a consequence of targeting by DNA methyltransferases.

Forensische DNA-Methylierungsanalyse

Die quantitative Analyse der relativen DNA-Methylierung gilt als eine der vielversprechendsten Methoden der molekularen Altersschätzung. Viele Studien der letzten Jahre identifizierten geeignete Positionen im Genom, deren DNA-Methylierung sich altersabhängig verändert. Für den Einsatz dieser Methode in der Routine- bzw. Fallarbeit ist es von großer Bedeutung, angewandte Analysetechniken zu validieren. Als ein Teilaspekt dieser Validierung sollte die Vergleichbarkeit der Analyseergebnisse zur DNA-Methylierung mithilfe der Mini- und Pyrosequenzierung zwischen verschiedenen Laboren evaluiert werden. Die Arbeitsgruppe „Molekulare Altersschätzung“ der Deutschen Gesellschaft für Rechtsmedizin (DGRM) führte hierzu den ersten, technischen Ringversuch durch, der 4 Positionen in den Genen PDE4C, EDARADD, SST und KLF14 umfasste. Diese Marker waren in vorangegangenen Studien als altersabhängige Biomarker charakterisiert worden. Am Ringversuch nahmen 12 Labore teil, wobei jedes die Wahl zwischen der Minisequenzierung und/oder der Pyrosequenzierung für die quantitative Methylierungsanalyse hatte. Jedem teilnehmenden Labor wurden Blut- und Speichelproben von 3 Personen unterschiedlichen Alters übersandt. Die Wahl der Reagenzien für die Probenbearbeitung wurde den Teilnehmern freigestellt.

Die Ergebnisse der Minisequenzierung zeigten systematische Abweichungen zwischen den Laboren, die am ehesten auf die Verwendung unterschiedlicher Reagenzien und Analyseplattformen zurückzuführen sein können. Die Resultate der Pyrosequenzierung hingegen wiesen nicht auf systematische Abweichungen zwischen den Laboren hin, hier zeigte sich jedoch die Tendenz einer markerabhängigen Abweichung. Darüber hinaus konnten Unterschiede hinsichtlich technischer Probleme zwischen Laboren mit mehr Erfahrung in der jeweiligen Sequenzierungsmethode und Laboren mit weniger Erfahrung festgestellt werden. Sowohl die Beobachtung von systematischen als auch die von markerabhängigen Abweichungen lässt den Schluss zu, dass eine Übertragung von Analysemethoden zwischen Laboren grundsätzlich möglich ist, eine Anpassung des jeweiligen Modells zur Altersschätzung jedoch notwendig sein kann.

Forensische DNA-Methylierungsanalyse

Mit der Entdeckung altersabhängiger epigenetischer Veränderungen, der DNA-Methylierung (DNAm), hat sich eine neue Möglichkeit aufgezeigt, das Alter eines Individuums zu schätzen. Die Methode wurde intensiv erforscht und ihre Anwendung in der forensischen Fallarbeit durch die Aktualisierung des § 81e der Strafprozessordnung (StPO) in Deutschland reguliert. Zur Untersuchung des DNAm-Grades müssen neue Techniken etabliert und validiert werden. Dies macht die Prüfung der Vergleichbarkeit von Messergebnissen aus verschiedenen forensischen Laboren erforderlich.

Hierzu führte die Arbeitsgruppe „Molekulare Altersschätzung“ der Deutschen Gesellschaft für Rechtsmedizin (DGRM) im Winter 2019/2020 den 2. Ringversuch (RV) zur quantitativen DNAm-Analyse mithilfe der Mini- und der Pyrosequenzierung durch. Dieser basierte auf den Erfahrungen des 1. RV 2018/2019, dessen Ergebnisse in dieser Ausgabe ebenfalls vorgestellt werden. Die aktuelle Studie umfasst Analyseergebnisse aus 12 Laboren (ingesamt 14 teilnehmende Labore), von denen einige beide Methoden angewandt haben. Zusätzlich führten 4 Labore eine Altersschätzung an den RV-Proben mit eigenen Markerkombinationen und Modellen durch. Da diese auf unterschiedlichen Referenzdaten und Markerkombinationen beruhen, erfolgte kein qualitativer Vergleich der Modelle, sondern das grundsätzliche Potenzial der Methodik wurde verdeutlicht. Ziele des RV waren die Evaluierung der Vergleichbarkeit der DNAm-Messungen und die Bewertung möglicher Einflussfaktoren, wie Extraktionsmethode und verwendetes Gerät.

Die Ergebnisse zeigen, dass sich die gemessenen DNAm-Werte der untersuchten Marker sowohl zwischen Mini- und Pyrosequenzierung als auch innerhalb der jeweiligen Methode zwischen den Laboren unterscheiden können, sodass mit Schwankungen gerechnet werden muss.

Development of the VISAGE enhanced tool and statistical models for epigenetic age estimation in blood, buccal cells and bones

DNA methylation is known as a biomarker for age with applications in forensics. Here we describe the VISAGE (VISible Attributes through GEnomics) Consortium's enhanced tool for epigenetic age estimation in somatic tissues. The tool is based on eight DNA methylation markers (44 CpGs), bisulfite multiplex PCR followed by sequencing on the MiSeq FGx platform, and three statistical prediction models for blood, buccal cells and bones. The model for blood is based on six CpGs from ELOVL2, MIR29B2CHG, KLF14, FHL2, TRIM59 and PDE4C, and predicts age with a mean absolute error (MAE) of 3.2 years, while the model for buccal cells includes five CpGs from PDE4C, MIR29B2CHG, ELOVL2, KLF14 and EDARADD and predicts age with MAE of 3.7 years, and the model for bones has six CpGs from ELOVL2, KLF14, PDE4C and ASPA and predicts age with MAE of 3.4 years. The VISAGE enhanced tool for age estimation in somatic tissues enables reliable collection of DNA methylation data from small amounts of DNA using a sensitive multiplex MPS assay that provides accurate estimation of age in blood, buccal swabs, and bones using the statistical model tailored to each tissue.

CTCF Mediates Replicative Senescence Through POLD1

POLD1, the catalytic subunit of DNA polymerase δ, plays a critical role in DNA synthesis and DNA repair processes. Moreover, POLD1 is downregulated in replicative senescence to mediate aging. In any case, the components of age-related downregulation of POLD1 expression have not been fully explained. In this article, we elucidate the mechanism of the regulation of POLD1 at the transcription level and found that the transcription factor CCCTC-binding factor (CTCF) was bound to the POLD1 promoter area in two sites. The binding level of CTCF for the POLD1 promoter appeared to be related to aging and was confirmed to be positively controlled by the CTCF level. Additionally, cell senescence characteristics were detected within the cells transfected with short hairpin RNA (shRNA)-CTCF, pLenti-CMV-CTCF, shRNA-POLD1, and pLenti-CMV-POLD1, and the results showed that the CTCF may contribute to the altered expression of POLD1 in aging. In conclusion, the binding level of CTCF for the POLD1 promoter intervened by an age-related decrease in CTCF and downregulated the POLD1 expression in aging. Moreover, the decrease in CTCF-mediated POLD1 transcription accelerates the progression of cell aging.

Targeted methods for epigenetic age predictions in mice

Age-associated DNA methylation reflects aspect of biological aging—therefore epigenetic clocks for mice can elucidate how the aging process in this model organism is affected by specific treatments or genetic background. Initially, age-predictors for mice were trained for genome-wide DNA methylation profiles and we have recently described a targeted assay based on pyrosequencing of DNA methylation at only three age-associated genomic regions. Here, we established alternative approaches using droplet digital PCR (ddPCR) and barcoded bisulfite amplicon sequencing (BBA-seq). At individual CG dinucleotides (CpGs) the correlation of DNA methylation with chronological age was slightly higher for pyrosequencing and ddPCR as compared to BBA-seq. On the other hand, BBA-seq revealed that neighboring CpGs tend to be stochastically modified at murine age-associated regions. Furthermore, the binary sequel of methylated and non-methylated CpGs in individual reads can be used for single-read predictions, which may reflect heterogeneity in epigenetic aging. In comparison to C57BL/6 mice the single-read age-predictions using BBA-seq were also accelerated in the shorter-lived DBA/2 mice, and in C57BL/6 mice with a lifespan quantitative trait locus of DBA/2 mice. Taken together, we describe alternative targeted methods for epigenetic age predictions that provide new perspectives for aging-intervention studies in mice.

Deconvolution of cellular subsets in human tissue based on targeted DNA methylation analysis at individual CpG sites

Background

The complex composition of different cell types within a tissue can be estimated by deconvolution of bulk gene expression profiles or with various single-cell sequencing approaches. Alternatively, DNA methylation (DNAm) profiles have been used to establish an atlas for multiple human tissues and cell types. DNAm is particularly suitable for deconvolution of cell types because each CG dinucleotide (CpG site) has only two states per DNA strand—methylated or non-methylated—and these epigenetic modifications are very consistent during cellular differentiation. So far, deconvolution of DNAm profiles implies complex signatures of many CpGs that are often measured by genome-wide analysis with Illumina BeadChip microarrays. In this study, we investigated if the characterization of cell types in tissue is also feasible with individual cell type-specific CpG sites, which can be addressed by targeted analysis, such as pyrosequencing.

Results

We compiled and curated 579 Illumina 450k BeadChip DNAm profiles of 14 different non-malignant human cell types. A training and validation strategy was applied to identify and test for cell type-specific CpGs. We initially focused on estimating the relative amount of fibroblasts using two CpGs that were either hypermethylated or hypomethylated in fibroblasts. The combination of these two DNAm levels into a “FibroScore” correlated with the state of fibrosis and was associated with overall survival in various types of cancer. Furthermore, we identified hypomethylated CpGs for leukocytes, endothelial cells, epithelial cells, hepatocytes, glia, neurons, fibroblasts, and induced pluripotent stem cells. The accuracy of this eight CpG signature was tested in additional BeadChip datasets of defined cell mixtures and the results were comparable to previously published signatures based on several thousand CpGs. Finally, we established and validated pyrosequencing assays for the relevant CpGs that can be utilized for classification and deconvolution of cell types.

Conclusion

This proof of concept study demonstrates that DNAm analysis at individual CpGs reflects the cellular composition of cellular mixtures and different tissues. Targeted analysis of these genomic regions facilitates robust methods for application in basic research and clinical settings.

SARS-CoV-2 and mitochondrial health: implications of lifestyle and ageing

Infection with SARs-COV-2 displays increasing fatality with age and underlying co-morbidity, in particular, with markers of the metabolic syndrome and diabetes, which seems to be associated with a "cytokine storm" and an altered immune response. This suggests that a key contributory factor could be immunosenescence that is both age-related and lifestyle-induced. As the immune system itself is heavily reliant on mitochondrial function, then maintaining a healthy mitochondrial system may play a key role in resisting the virus, both directly, and indirectly by ensuring a good vaccine response. Furthermore, as viruses in general, and quite possibly this new virus, have also evolved to modulate immunometabolism and thus mitochondrial function to ensure their replication, this could further stress cellular bioenergetics. Unlike most sedentary modern humans, one of the natural hosts for the virus, the bat, has to "exercise" regularly to find food, which continually provides a powerful adaptive stimulus to maintain functional muscle and mitochondria. In effect the bat is exposed to regular hormetic stimuli, which could provide clues on how to resist this virus. In this paper we review the data that might support the idea that mitochondrial health, induced by a healthy lifestyle, could be a key factor in resisting the virus, and for those people who are perhaps not in optimal health, treatments that could support mitochondrial function might be pivotal to their long-term recovery.

Genetic and environmental causes of variation in epigenetic aging across the lifespan

Background

DNA methylation-based biological age (DNAm age) is an important biomarker for adult health. Studies in specific age ranges have found widely varying results about its genetic and environmental causes of variation. However, these studies are not able to provide a comprehensive view of the causes of variation over the lifespan.

Results

In order to investigate the genetic and environmental causes of DNAm age variation across the lifespan, we pooled genome-wide DNA methylation data for 4217 people aged 0–92 years from 1871 families. DNAm age was calculated using the Horvath epigenetic clock. We estimated familial correlations in DNAm age for monozygotic (MZ) twin, dizygotic (DZ) twin, sibling, parent–offspring, and spouse pairs by cohabitation status. Genetic and environmental variance components models were fitted and compared. We found that twin pair correlations were − 0.12 to 0.18 around birth, not different from zero (all P > 0.29). For all pairs of relatives, their correlations increased with time spent living together (all P < 0.02) at different rates (MZ > DZ and siblings > parent–offspring; P < 0.001) and decreased with time spent living apart (P = 0.02) at similar rates. These correlation patterns were best explained by cohabitation-dependent shared environmental factors, the effects of which were 1.41 (95% confidence interval [CI] 1.16 to 1.66) times greater for MZ pairs than for DZ and sibling pairs, and the latter were 2.03 (95% CI 1.13 to 9.47) times greater than for parent–offspring pairs. Genetic factors explained 13% (95% CI − 10 to 35%) of variation (P = 0.27). Similar results were found for another two epigenetic clocks, suggesting that our observations are robust to how DNAm age is measured. In addition, results for the other clocks were consistent with there also being a role for prenatal environmental factors in determining their variation.

Conclusions

Variation in DNAm age is mostly caused by environmental factors, including those shared to different extents by relatives while living together and whose effects persist into old age. The equal environment assumption of the classic twin study might not hold for epigenetic aging.

PRDM8 reveals aberrant DNA methylation in aging syndromes and is relevant for hematopoietic and neuronal differentiation

BACKGROUND

Dyskeratosis congenita (DKC) and idiopathic aplastic anemia (AA) are bone marrow failure syndromes that share characteristics of premature aging with severe telomere attrition. Aging is also reflected by DNA methylation changes, which can be utilized to predict donor age. There is evidence that such epigenetic age predictions are accelerated in premature aging syndromes, but it is yet unclear how this is related to telomere length. DNA methylation analysis may support diagnosis of DKC and AA, which still remains a challenge for these rare diseases.

RESULTS

In this study, we analyzed blood samples of 70 AA and 18 DKC patients to demonstrate that their epigenetic age predictions are overall increased, albeit not directly correlated with telomere length. Aberrant DNA methylation was observed in the gene PRDM8 in DKC and AA as well as in other diseases with premature aging phenotype, such as Down syndrome and Hutchinson-Gilford-Progeria syndrome. Aberrant DNA methylation patterns were particularly found within subsets of cell populations in DKC and AA samples as measured with barcoded bisulfite amplicon sequencing (BBA-seq). To gain insight into the functional relevance of PRDM8, we used CRISPR/Cas9 technology to generate induced pluripotent stem cells (iPSCs) with heterozygous and homozygous knockout. Loss of PRDM8 impaired hematopoietic and neuronal differentiation of iPSCs, even in the heterozygous knockout clone, but it did not impact on epigenetic age.

CONCLUSION

Taken together, our results demonstrate that epigenetic aging is accelerated in DKC and AA, independent from telomere attrition. Furthermore, aberrant DNA methylation in PRDM8 provides another biomarker for bone marrow failure syndromes and modulation of this gene in cellular subsets may be related to the hematopoietic and neuronal phenotypes observed in premature aging syndromes.

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.