Reversal of epigenetic aging and immunosenescent trends in humans

Epigenetic “clocks” can now surpass chronological age in accuracy for estimating biological age. Here, we use four such age estimators to show that epigenetic aging can be reversed in humans. Using a protocol intended to regenerate the thymus, we observed protective immunological changes, improved risk indices for many age‐related diseases, and a mean epigenetic age approximately 1.5 years less than baseline after 1 year of treatment (?2.5‐year change compared to no treatment at the end of the study). The rate of epigenetic aging reversal relative to chronological age accelerated from ?1.6 year/year from 0–9 month to ?6.5 year/year from 9–12 month. The GrimAge predictor of human morbidity and mortality showed a 2‐year decrease in epigenetic vs. chronological age that persisted six months after discontinuing treatment. This is to our knowledge the first report of an increase, based on an epigenetic age estimator, in predicted human lifespan by means of a currently accessible aging intervention.     

Life spanning murine gene expression profiles in relation to chronological and pathological aging in multiple organs

Aging and age‐related pathology is a result of a still incompletely understood intricate web of molecular and cellular processes. We present a C57BL/6J female mice in vivo aging study of five organs (liver, kidney, spleen, lung, and brain), in which we compare genome‐wide gene expression profiles during chronological aging with pathological changes throughout the entire murine life span (13, 26, 52, 78, 104, and 130 weeks). Relating gene expression changes to chronological aging revealed many differentially expressed genes (DEGs), and altered gene sets (AGSs) were found in most organs, indicative of intraorgan generic aging processes. However, only ≤ 1% of these DEGs are found in all organs. For each organ, at least one of 18 tested pathological parameters showed a good age‐predictive value, albeit with much inter‐ and intraindividual (organ) variation. Relating gene expression changes to pathology‐related aging revealed correlated genes and gene sets, which made it possible to characterize the difference between biological and chronological aging. In liver, kidney, and brain, a limited number of overlapping pathology‐related AGSs were found. Immune responses appeared to be common, yet the changes were specific in most organs. Furthermore, changes were observed in energy homeostasis, reactive oxygen species, cell cycle, cell motility, and DNA damage. Comparison of chronological and pathology‐related AGSs revealed substantial overlap and interesting differences. For example, the presence of immune processes in liver pathology‐related AGSs that were not detected in chronological aging. The many cellular processes that are only found employing aging‐related pathology could provide important new insights into the progress of aging.     

Tissue‐disruption‐induced cellular stochasticity and epigenetic drift: Common origins of aging and cancer?

Age‐related and cancer‐related epigenomic modifications have been associated with enhanced cell‐to‐cell gene expression variability that characterizes increased cellular stochasticity. Since gene expression variability appears to be highly reduced by—and epigenetic and phenotypic stability acquired through—direct or long‐range cellular interactions during cell differentiation, we propose a common origin for aging and cancer in the failure to control cellular stochasticity by cell–cell interactions. Tissue‐disruption‐induced cellular stochasticity associated with epigenetic drift would be at the origin of organ dysfunction because of an increase in phenotypic variation among cells, ultimately leading to cell death and organ failure through a loss of coordination in cellular functions, and eventually to cancerization. We propose mechanistic research perspectives to corroborate this hypothesis and explore its evolutionary consequences, highlighting a positive correlation between the median age of mass loss onset (a proxy for the onset of organ aging) and the median age at cancer diagnosis.     

DNA methylation and healthy human aging

The process of aging results in a host of changes at the cellular and molecular levels, which include senescence, telomere shortening, and changes in gene expression. Epigenetic patterns also change over the lifespan, suggesting that epigenetic changes may constitute an important component of the aging process. The epigenetic mark that has been most highly studied is DNA methylation, the presence of methyl groups at CpG dinucleotides. These dinucleotides are often located near gene promoters and associate with gene expression levels. Early studies indicated that global levels of DNA methylation increase over the first few years of life and then decrease beginning in late adulthood. Recently, with the advent of microarray and next‐generation sequencing technologies, increases in variability of DNA methylation with age have been observed, and a number of site‐specific patterns have been identified. It has also been shown that certain CpG sites are highly associated with age, to the extent that prediction models using a small number of these sites can accurately predict the chronological age of the donor. Together, these observations point to the existence of two phenomena that both contribute to age‐related DNA methylation changes: epigenetic drift and the epigenetic clock. In this review, we focus on healthy human aging throughout the lifetime and discuss the dynamics of DNA methylation as well as how interactions between the genome, environment, and the epigenome influence aging rates. We also discuss the impact of determining ‘epigenetic age’ for human health and outline some important caveats to existing and future studies.     

MOABS: model based analysis of bisulfite sequencing data

Background

Human aging is associated with DNA methylation changes at specific sites in the genome. These epigenetic modifications may be used to track donor age for forensic analysis or to estimate biological age.

Results

We perform a comprehensive analysis of methylation profiles to narrow down 102 age-related CpG sites in blood. We demonstrate that most of these age-associated methylation changes are reversed in induced pluripotent stem cells (iPSCs). Methylation levels at three age-related CpGs - located in the genes ITGA2B, ASPA and PDE4C - were subsequently analyzed by bisulfite pyrosequencing of 151 blood samples. This epigenetic aging signature facilitates age predictions with a mean absolute deviation from chronological age of less than 5 years. This precision is higher than age predictions based on telomere length. Variation of age predictions correlates moderately with clinical and lifestyle parameters supporting the notion that age-associated methylation changes are associated more with biological age than with chronological age. Furthermore, patients with acquired aplastic anemia or dyskeratosis congenita - two diseases associated with progressive bone marrow failure and severe telomere attrition - are predicted to be prematurely aged.

Conclusions

Our epigenetic aging signature provides a simple biomarker to estimate the state of aging in blood. Age-associated DNA methylation changes are counteracted in iPSCs. On the other hand, over-estimation of chronological age in bone marrow failure syndromes is indicative for exhaustion of the hematopoietic cell pool. Thus, epigenetic changes upon aging seem to reflect biological aging of blood.     

The use of DNA methylation clock in aging research

One of the key characteristics of aging is a progressive loss of physiological integrity, which weakens bodily functions and increases the risk of death. A robust biomarker is important for the assessment of biological age, the rate of aging, and a person''s health status. DNA methylation clocks, novel biomarkers of aging, are composed of a group of cytosine-phosphate-guanine dinucleotides, the DNA methylation status of which can be used to accurately measure subjective age. These clocks are considered accurate biomarkers of chronological age for humans and other vertebrates. Numerous studies have demonstrated these clocks to quantify the rate of biological aging and the effects of longevity and anti-aging interventions. In this review, we describe the purpose and use of DNA methylation clocks in aging research.     

A metabolic signature predicts biological age in mice

Our understanding of the mechanisms by which aging is produced is still very limited. Here, we have determined the sera metabolite profile of 117 wild‐type mice of different genetic backgrounds ranging from 8 to 129 weeks of age. This has allowed us to define a robust metabolomic signature and a derived metabolomic score that reliably/accurately predicts the age of wild‐type mice. In the case of telomerase‐deficient mice, which have a shortened lifespan, their metabolomic score predicts older ages than expected. Conversely, in the case of mice that overexpress telomerase, their metabolic score corresponded to younger ages than expected. Importantly, telomerase reactivation late in life by using a TERT‐based gene therapy recently described by us significantly reverted the metabolic profile of old mice to that of younger mice, further confirming an anti‐aging role for telomerase. Thus, the metabolomic signature associated with natural mouse aging accurately predicts aging produced by telomere shortening, suggesting that natural mouse aging is in part produced by presence of short telomeres. These results indicate that the metabolomic signature is associated with the biological age rather than with the chronological age. This constitutes one of the first aging‐associated metabolomic signatures in a mammalian organism.     

Analysis of epigenetic aging in vivo and in vitro: Factors controlling the speed and direction

The mechanism of aging is not yet fully understood. It has been recognized that there are age-dependent changes in the DNA methylation pattern of the whole genome. To date, there are several DNA methylation-based estimators of the chronological age. A majority of the estimators use the DNA methylation data from a single tissue type, such as blood. In 2013, for the first time, Steve Horvath reported the DNA methylation-based age estimator (353 CpGs were used) that could be applied to multiple tissues. A refined, more sensitive version that uses 391 CpGs was subsequently developed and validated in human cells, including fibroblasts. In this review, the age predicted by DNA methylation-based age estimator is referred to as DNAmAge, and the biological process controlling the progression of DNAmAge is referred to as the epigenetic aging in this minireview. The concepts of DNAmAge and epigenetic aging provide us opportunities to discover previously unrecognized biological events controlling aging. In this article, we discuss the frequently asked questions regarding DNAmAge and the epigenetic aging by introducing recent studies of ours and others. We focus on addressing the following questions: (1) Is there any synchronization of DNAmAge between cells in a human body?, (2) Can we use in vitro (cell culture) systems to study the epigenetic aging?, (3) Is there an age limit of DNAmAge?, and (4) Is it possible to change the speed and direction of the epigenetic aging? We describe our current understandings to these questions and outline potential future directions.Impact statementAging is associated with DNA methylation (DNAm) changes. Recent advancement of the whole-genome DNAm analysis technology allowed scientists to develop DNAm-based age estimators. A majority of these estimators use DNAm data from a single tissue type such as blood. In 2013, a multi-tissue age estimator using DNAm pattern of 353 CpGs was developed by Steve Horvath. This estimator was named “epigenetic clock”, and the improved version using DNAm pattern of 391 CpGs was developed in 2018. The estimated age by epigenetic clock is named DNAmAge. DNAmAge can be used as a biomarker of aging predicting the risk of age-associated diseases and mortality. Although the DNAm-based age estimators were developed, the mechanism of epigenetic aging is still enigmatic. The biological significance of epigenetic aging is not well understood, either. This minireview discusses the current understanding of the mechanism of epigenetic aging and the future direction of aging research.     

The association between adverse childhood experiences and epigenetic age acceleration in the Canadian longitudinal study on aging (CLSA)

Research examining the association between exposure to a wide range of adverse childhood experiences (ACEs) and accelerated biological aging in older adults is limited. The purpose of this study was to examine the association of ACEs, both as a cumulative score and individual forms of adversity, with epigenetic age acceleration assessed using the DNA methylation (DNAm) GrimAge and DNAm PhenoAge epigenetic clocks in middle and older-aged adults. This cross-sectional study analyzed baseline and first follow-up data on 1445 participants aged 45–85 years from the Canadian Longitudinal Study on Aging (CLSA) who provided blood samples for DNAm analysis. ACEs were assessed using a validated self-reported questionnaire. Epigenetic age acceleration was estimated by regressing each epigenetic clock estimate on chronological age. Cumulative ACEs score was associated with higher DNAm GrimAge acceleration (β: 0.07; 95% CI: 0.02, 0.11) after adjusting for covariates. Childhood exposure to parental separation or divorce (β: 0.06; 95% CI: 0.00, 0.11) and emotional abuse (β: 0.06; 95% CI: 0.00, 0.12) were associated with higher DNAm GrimAge acceleration after adjusting for other adversities and covariates. There was no statistical association between ACEs and DNAm PhenoAge acceleration. Early life adversity may become biologically embedded and lead to premature biological aging, in relation to DNAm GrimAge, which estimates risk of mortality. Strategies that increase awareness of ACEs and promote healthy child development are needed to prevent ACEs.     

Markers of cellular senescence in zero hour biopsies predict outcome in renal transplantation

Although chronological donor age is the most potent predictor of long-term outcome after renal transplantation, it does not incorporate individual differences of the aging-process itself. We therefore hypothesized that an estimate of biological organ age as derived from markers of cellular senescence in zero hour biopsies would be of higher predictive value. Telomere length and mRNA expression levels of the cell cycle inhibitors CDKN2A (p16INK4a) and CDKN1A (p21WAF1) were assessed in pre-implantation biopsies of 54 patients and the association of these and various other clinical parameters with serum creatinine after 1 year was determined. In a linear regression analysis, CDKN2A turned out to be the best single predictor followed by donor age and telomere length. A multiple linear regression analysis revealed that the combination of CDKN2A values and donor age yielded even higher predictive values for serum creatinine 1 year after transplantation. We conclude that the molecular aging marker CDKN2A in combination with chronological donor age predict renal allograft function after 1 year significantly better than chronological donor age alone.     

Circulating levels of monocyte chemoattractant protein‐1 as a potential measure of biological age in mice and frailty in humans

A serum biomarker of biological versus chronological age would have significant impact on clinical care. It could be used to identify individuals at risk of early‐onset frailty or the multimorbidities associated with old age. It may also serve as a surrogate endpoint in clinical trials targeting mechanisms of aging. Here, we identified MCP‐1/CCL2, a chemokine responsible for recruiting monocytes, as a potential biomarker of biological age. Circulating monocyte chemoattractant protein‐1 (MCP‐1) levels increased in an age‐dependent manner in wild‐type (WT) mice. That age‐dependent increase was accelerated in Ercc1^?/Δ and Bubr1^H/H mouse models of progeria. Genetic and pharmacologic interventions that slow aging of Ercc1^?/Δ and WT mice lowered serum MCP‐1 levels significantly. Finally, in elderly humans with aortic stenosis, MCP‐1 levels were significantly higher in frail individuals compared to nonfrail. These data support the conclusion that MCP‐1 can be used as a measure of mammalian biological age that is responsive to interventions that extend healthy aging.     

Age estimation in a long‐lived seabird (Ardenna tenuirostris) using DNA methylation‐based biomarkers

Age structure is a fundamental aspect of animal population biology. Age is strongly related to individual physiological condition, reproductive potential and mortality rate. Currently, there are no robust molecular methods for age estimation in birds. Instead, individuals must be ringed as chicks to establish known‐age populations, which is a labour‐intensive and expensive process. The estimation of chronological age using DNA methylation (DNAm) is emerging as a robust approach in mammals including humans, mice and some non‐model species. Here, we quantified DNAm in whole blood samples from a total of 71 known‐age Short‐tailed shearwaters (Ardenna tenuirostris) using digital restriction enzyme analysis of methylation (DREAM). The DREAM method measures DNAm levels at thousands of CpG dinucleotides throughout the genome. We identified seven CpG sites with DNAm levels that correlated with age. A model based on these relationships estimated age with a mean difference of 2.8 years to known age, based on validation estimates from models created by repeated sampling of training and validation data subsets. Longitudinal observation of individuals re‐sampled over 1 or 2 years generally showed an increase in estimated age (6/7 cases). For the first time, we have shown that epigenetic changes with age can be detected in a wild bird. This approach should be of broad interest to researchers studying age biomarkers in non‐model species and will allow identification of markers that can be assessed using targeted techniques for accurate age estimation in large population studies.