Epigenetic clock

History

The strong effects of age on DNA methylation levels have been known since the late 1960s. ^[5] A vast literature Describes sets of CpGs Whose DNA methylation levels correlate with age, eg ^{[6] [7] [8] [9] [10]} Two publications describe age estimators based on DNA methylation levels in Either saliva ^[11] or blood. ^[12]

Horvath’s epigenetic clock was developed by Steve Horvath , a professor of human genetics at the David Geffen School of Medicine at UCLA and of biostatistics at the UCLA Fielding School of Public Health. The scientific article was first published on Oct 21, 2013, in Genome Biology . ^[1] [3] Horvath spent over 4 years. ^[13] The personal story behind the discovery was featured in Nature. ^[14]The age estimator was developed using 8,000 samples from 82 Illumina DNA methylation array datasets, encompassing 51 healthy tissues and cell types. The major innovation of Horvath’s epigenetic clock lies in its wide applicability: the same set of 353 CpGs and the same prediction algorithm is used irrespective of the DNA source within the organization, ie it does not require any adjustments or offsets. ^[1] This property allows one to compare the ages of different areas of the human body using the same aging clock.

Relationship to a cause of biological aging

It is not yet known what exactly is measured by DNA methylation age. Horvath hypothesized that the effects of methylation are cumulative. The fact that DNA methylation predicts all-cause mortality in later life ^{[15] [16] [17] [18]} . ^[19]However, it is unlikely that the 353 clock CpGs are special or play a direct causal role in the aging process. ^[1] Rather, the epigenetic clock captures an emergent property of the epigenome.

Motivation for biological clocks

In general, biological aging clocks and biomarkers of aging are expected to be found in a fundamental characteristic of most organisms . Accurate Measures of biological age ( biological aging clocks ) Could Be Useful for

• testing the validity of various theories of biological aging ,
• Diagnosing various age related diseases and for defining cancer subtypes,
• predicting / prognosticating the onset of various diseases,
• as surrogate markers for therapeutic intervention, including rejuvenation approaches,
• studying developmental biology and cell differentiation ,
• Forensic applications, for example to estimate the age of a suspect based on a crime scene.

Overall, biological clocks are expected to be useful for studying what causes aging and what can be done against it.

Properties of Horvath’s clock

The clock is defined as an age estimation method based on 353 epigenetic markers on the DNA. The 353 markers measure DNA methylation of CpG dinucleotides . The present invention is also useful for predicting age (“predicted age” in mathematical use), and has the following properties: first, it is close to zero for embryonic and induced pluripotent stem cells ; second, it correlates with cell passage number; third, it gives rise to a highly heritable measure of age acceleration; and, fourth, it is applicable to chimpanzee tissues (which are used as human analogs for biological testing purposes). Organismal growth (and concomitant cell division) leads to a high ticking rate of the epigenetic clock that slows down to a constant ticking rate (linear dependence) after adulthood (age 20). ^[1] The fact That DNA methylation age of blood Predicts all-causes mortality in later life Even After adjusting for risk factors Known ^[15] [16]Suggests That It concerne un That process causes aging. Similarly, markers of physical and mental fitness are associated with the epigenetic clock. ^[20]

Salient features of Horvath’s epigenetic clock include its high accuracy and its applicability to a broad spectrum of tissues and cell types. Since it allows one to contrast the ages of different tissues from the same subject, it can be used to identify tissues that show evidence of accelerated age of disease.

Statistical approach

The basic approach is to form a weighted average of the 353 clock CpGs, which is then converted to a calibration using a calibration function. The calibration function reveals that the epigenetic clock has a high ticking rate until adulthood, after which it slows to a constant ticking rate. Using the training data sets, Elastic net regularizationCalibration of the chronological age on 21,369 CpG probes that were present both on the Illumina 450K and 27K platform and had fewer than 10 missing values. DNAm age is defined as estimated (“predicted”) age. The elastic net predictor automatically selected 353 CpGs. CpGs correlate negatively with age. R software and a freely available web-based tool can be found at the following webpage. ^[21]

Accuracy

The median error is estimated over a wide range of tissues and cell types. ^[1] The epigenetic clock performs well in heterogeneous tissues (for example, whole blood, peripheral blood mononuclear cells, cerebellar samples, occipital cortex, oral epithelium, colon, adipose, kidney, liver, lung, saliva, uterine cervix, epidermis, muscle CD4 T cells, CD14 monocytes, glial cells, neurons, immortalized B cells, mesenchymal stromal cells. ^[1] However, accuracy depends on some extent on the source of the DNA.

Comparison with other biological clocks

The epigenetic clock leads to a chronological age prediction That: has a Pearson correlation coefficient of r = 0.96 with chronological age (Figure 2 in ^[1] ). Thus the age correlation is close to its maximum possible correlation value of 1. Other biological clocks are based on a) telomere length, b) p16INK4a expression levels (also known as INK4a / ARF locus), ^[22] and c) microsatellite mutations. ^[23] The correlation between chronological age and telomere length is r = -0.51 in women and r = -0.55 in men. ^[24] The correlation between chronological age and expression levels of p16INK4ain T cells is r = 0.56. ^[25] p16INK4a expression levels only related to age in T cells , a type of white blood cells. ^{[ citation needed ]} The microsatellite clock measures the chronological age of divisions within a tissue. ^{[ quote needed ]}

Comparison with wild mammal biological clocks

Wang et al., (In mice livers) ^[26] and Petkovich et al., (Based on the mice blood DNA methylation profiles) ^[27] reviewed whether mice and humans experience similar patterns of change in methylome with age. They found that mice treated with lifespan-extending procedures (superch as calorie restriction or dietary rapamycin) were significantly older in their epigenetic age than their untreated, wild-type age-matched controls. Mice age predictors the effects of gene knockouts, and rejuvenation of fibroblast-derived iPSCs.

Mice multi-tissue age prediction based on DNA methylation at 329 unique CpG sites reached a median absolute error of less than 4 weeks (~ 5% of lifespan). An attempt to use the human clock shows that the human clock is not fully conserved in a mouse. ^[28] Differences between human and mouse clocks that suggest different epigenetic clocks. ^[29]

Changes to DNA methylation patterns have great potential for age estimation and biomarker search in domestic and wild animals. ^[30]

Applications of Horvath’s clock

By contrasting DNA age (age) with chronological age, one can define measures of age acceleration. Age acceleration can be defined as the difference between DNA methylation and chronological age. Alternatively, it can be defined as the residual results of regressing DNA age on chronological age. The latter measure is attractive because it does not correlate with chronological age. A positive / negative value of epigenetic age acceleration suggests that the underlying tissue ages faster / slower than expected.

Genetic studies of epigenetic age acceleration

The broad sense of heritability (defined by Falconer’s formula ) of age is more important than newborns. ^[1] Similarly, the age of acceleration of brain tissue (prefrontal cortex) was found to be 41% in older subjects. ^[31] Genome-wide association studies of cerebellar age acceleration have identified several SNPs at a genomewide significance level. ^[32] [33] Gene and SNP sets forth by Genome-Wide Association of Epigenetic Acceleration of Alzheimer’s disease, age-related macular degeneration, and Parkinson’s disease. ^[32] [33]

Female breast tissue is older than expected

DNAm age is higher than chronological age in breast cancer that is adjacent to breast cancer tissue. ^[1] Since it is possible that the body does not exhibit a similar type ^[1] Similarly, PMID  28364215, PMID 28364215 .

Cancer tissue

Cancer tissues show both positive and negative age acceleration effects. For most tumor types, no significant relationship can be observed between age and tumor morphology (grade / stage). ^[1] [2] On average, cancer tissues with mutated TP53 have a lower age of acceleration than those without it. ^[1] Further, cancer tissues with high age acceleration tend to have fewer somatic mutations than those with low age acceleration. ^[1] [2] Age acceleration is highly related to various genomic aberrations in cancer tissues. Somatic mutations in estrogen receptors or progesterone receptors are associated with accelerated DNA age in breast cancer. ^[1]Colorectal cancer samples with a BRAF(V600E) mutation or promoter hypermethylation of the mismatch repair gene MLH1 are associated with an increased age acceleration. ^[1] Age of acceleration in glioblastoma multiform samples is very much associated with certain mutations in H3F3A . ^[1] One study suggests that the epigenetic age of blood may be prognostic of lung cancer incidence. ^[34]

Obesity and metabolic syndrome

The epigenetic clock was used to study the relationship between high body mass index (BMI) and the DNA methylation of human blood, liver, muscle and adipose tissue. ^[35] A significant correlation (r = 0.42) between BMI and epigenetic age acceleration could be observed for the liver. A much larger sample size (n = 4200 blood samples) revealed a weak but statistically significant correlation (r = 0.09) between BMI and intrinsic age acceleration of blood PMID  28198702 . The same large study found that various biomarkers of metabolic syndrome (glucose-, insulin-, triglyceride levels, C-reactive protein, waist-to-hip ratio ) were associated with epigenetic age acceleration in blood PMID. 28198702 . Conversely, high levels of good HDL cholesterol were associated with PMID  28198702 .

Trisomy 21 (Down syndrome)

Down Syndrome (DS) entails an increased risk of many chronic diseases that are associated with older age. The clinical manifestations of accelerated aging suggest that trisomy 21 increases the biological age of tissues, but molecular evidence for this hypothesis has been sparse. According to the epigenetic clock, trisomy 21 significantly increases the age of blood and brain tissue (on average by 6.6 years). ^[36]

Alzheimer’s disease related neuropathology

Epigenetic age acceleration of the human prefrontal cortex was found to be correlated with several neuropathological measures that play a role in Alzheimer’s disease ^[31] Further, it was found to be associated with a decline in global cognitive functioning, and memory functioning among Alzheimer’s patients. disease. ^[31] The epigenetic age of blood relates to cognitive functioning in the elderly. ^[20] Overall, these results strongly suggest that the epigenetic clock reads itself for measuring the biological age of the brain.

Cerebellum ages slowly

It has been difficult to identify tissues that seem to be less effective than others in comparison to other tissues. An application of epigenetic clock to 30 anatomical sites from six centenarians and younger subjects revealed that the cerebellum ages slowly: it is about 15 years younger than expected in a centenarian. ^[37] This finding may explain why the cerebellum exhibits fewer neuropathological hallmarks of age related dementia compared to other brain regions. In younger subjects (eg younger than 70), brain regions and brain cells appear to have roughly the same age. ^[1] [37] Several SNPs and genes have been identified that relate to the epigenetic age of the cerebellum^[32]

Huntington’s disease

Huntington’s disease has been found to increase the epigenetic aging rates of several human brain regions. ^[38]

Centenarians age slowly

The offspring of semi-supercentenarians (age group of 105-109 years) are more likely than age-matched controls chronological age. ^[18]

HIV infection

Infection with the Human Immunodeficiency Virus-1 ( HIV ) is associated with higher rates of accelerated aging, as evidenced by increased incidence and diversity of age-related conditions. But it has been difficult to detect an accelerated aging effect on a molecular level. An epigenetic clock analysis of human DNA from HIV + subjects and controls detected in a significant age in the brain (7.4 years) and blood (5.2 years) due to HIV-1 infection. ^[39]

Parkinson’s disease

A large-scale study suggests that the blood of Parkinson’s disease subjects exhibits (“relatively weak”) accelerated aging effects. ^[40]

Developmental disorder: syndrome X

Children with a very rare disorder Known as syndrome X Maintain the facade of persistent toddler-like features while aging from birth to adulthood. Since these things are dramatically delayed, these children appear to be at or near a best preschooler. According to an epigenetic clock analysis, blood tissue from X syndrome is not younger than expected. ^[41]

Menopause accelerates epigenetic aging

The following results strongly suggest that the loss of female hormones results from menopause accelerates the epigenetic aging rate of blood and possibly that of other tissues. ^[42] First, early menopause has been found to be associated with an increased epigenetic age acceleration of blood. ^[42] Second, surgical menopause (due to bilateral oophorectomy ) is associated with epigenetic age acceleration in blood and saliva. Third, menopausal hormone therapy , which mitigates hormonal loss, is associated with a negative age of acceleration of buccal cells (but not of blood cells). ^[42]Fourth, genetic markers that are associated with early menopause are also associated with increased epigenetic age acceleration in blood. ^[42]

Cellular senescence versus epigenetic aging

A confounding aspect of biological aging is the nature and role of senescent cells. It is unclear whether the three major types of cellular senescence, namely, replicative senescence, oncogene-induced senescence, and DNA damage-induced senescence are described by the same phenomenon. with epigenetic aging. Induction of replicative senescence (RS) and oncogene-induced senescence (OIS) were also associated with epigenetic aging of primary cells, but RS and OIS activate the cellular DNA damage response pathway. ^[43]These results highlight the independence of cellular senescence from epigenetic aging. Consisted with this, telomerase-immortalized cells continued to age (with the epigenetic clock) without any inducing agent or agent, or re-affirming the independence of the process of epigenetic aging of telomeres, cellular senescence, and the DNA damage response pathway. Although the uncoupling of senescence from cellular aging appears at first sight to be inconsistent with the fact that senescent cells contribute to the physical manifestation of organism aging, as demonstrated by Baker et al., Where removal of senescent cells slowed down. ^[44]However, the epigenetic clock of senescence suggests that cellular senescence is a state of the art, which is characterized by a high degree of degradation, which is an expression of the ability to express and suppress the proliferation of cells. ^[43]These senescent cells, in sufficient numbers, will probably cause the deterioration of tissues, which is interpreted as organism aging. However, at the cellular level, aging, as measured by the epigenetic clock, is distinct from senescence. It is an intrinsic mechanism that exists from the birth of the cell and continues. This implies that they are not going to be affected by the fact that they would still continue to age. Such telometers are likely to have long-term telomeres, although their telomere lengths are substantially longer than the critical limit, and they are prematurely when their telomere is forcibly shortened, due to replicative senescence. Therefore, cellular senescence is a road by which cells exit prematurely from the natural course of cellular aging.^[43]

Effect of sex and race / ethnicity

Men age faster than women according to epigenetic age acceleration in blood, brain, saliva, and many other tissues. ^[45] The epignetic clock method applies to all examined racial / ethnic groups in the sense that DNAm age is highly correlated with chronological age. But ethnicity can be associated with epigenetic age acceleration. ^[45] For example, the blood of Hispanics and the Tsimane ages more than that of other populations which might explain the Hispanic mortality paradox . ^[45]

Rejuvenation effect stem cell transplantation in blood

Hematopoietic stem cell transplantation , which transplants these cells from a young donor to an older recipient, rejuvenates the epigenetic age of blood to that of the donor PMID  28550187 . However, graft-versus-host disease is associated with increased DNA methyhlation age PMID  28550187 .

Progeria

Is also known as Werner syndrome is associated with epigenetic age acceleration in blood. ^[46]

Biological mechanism behind the epigenetic clock

The precise biological mechanism behind the epigenetic clock is currently unknown. However, the following explanations have been proposed in the literature.

Possible explanation 1: Epigenomic maintenance system

Horvath hypothesized that his clock arises from a methylation footprint left by an epigenomic maintenance system. ^[1]

Possible explanation 2: Unrepaired DNA damages

Endogenous DNA damage occurs in a 50-fold double-strand DNA breaks per cell cycle ^[47] and about 10,000 oxidative damages per day (see DNA damage (naturally occurring) ). During repair and replacement of epigenetic alterations are also reported, including an increase in the percentage of epigenetic alterations remaining after repair is completed, including increased methylation of CpG island promoters. ^{[48] [49] [50]} Similar, but usually transient epigenetic alterations were recently found during repair of oxidative damage caused by H ₂ O ₂ , and it was suggested that these epigenetic alterations may also remain after repair. ^[51]These accumulated epigenetic alterations may contribute to the epigenetic clock. Accumulation of epigenetic alterations may parallel the accumulation of a-repaired DNA damage that is proposed to cause aging (see DNA damage theory of aging ).

Other age estimates based on DNA methylation levels

Several other age estimators have been described in the literature.

1) Weidner et al. (2014) describe an age estimator for DNA as a result of aging (cG25229905 in integrin, alpha 2b (ITGA2B); cg02228185 in aspartoacylase (ASPA) and cg17861230 in phosphodiesterase 4C, cAMP specific (PDE4C )). ^[52] The age estimator by Weidener et al. (2014) applies only to blood. Even in blood this sparse estimator is far less accurate than Horvath’s epigenetic clock (Horvath 2014) when applied to data generated by the Illumina 27K or 450K platforms. ^[53] But the sparse estimator was developed for pyrosequencing data and is highly cost effective. ^[54]

2) Hannum et al. (2013) ^[12]multiple ages estimators: one for each tissue type. Each of these estimators requires covariate information (eg gender, body mass index, batch). The authors mention that each fabric has a clear linear offset (intercept and slope). Therefore, the authors had to adjust the blood-based age estimator for each tissue type using a linear model. When the Hannum estimator is applied to other tissues, it leads to a high error (Figure 4A in Hannum et al. (2013). Hannum et al. their blood-based age estimator (by adjusting the slope and the intercept term) in order to apply it to other tissue types. Since this adjustment step removes differences between tissue, the blood-based estimator from Hannum et al. can not be used to compare the ages of different tissues / organs. In contrast,^[1] it always uses the same CpGs and the same coefficient values. Therefore, Horvath’s epigenetic clock can be used to compare tissues / cells / organs from the same individual. While the age estimators from Hannum et al. They can be used to compare normal tissues with normal tissue (non-cancerous) tissue. Hannum et al. pronounced age acceleration effects in all cancers. In contrast, Horvath’s Epigenetic Clock ^[2] [55]reveals that some cancer types (eg triple negative breast cancers or uterine corpus endometrial carcinoma) exhibit negative age acceleration, ie cancer tissue can be much younger than expected. An important difference relates to additional covariates. Hannum’s age estimators make use of covariates such as gender, body mass index, diabetes status, ethnicity, and batch. Since we are new to batches, we can not apply it directly to new data. However, the authors present coefficient values ​​for their CpGs in Supplementary Tables which can be used to define an aggregate measure (ie, lead to high errors).

3.) Giuliani et al. identify genomic regions whose DNA methylation level correlates with age in human teeth. They propose the evaluation of DNA methylation at ELOVL2, FHL2, and PENK genes in DNA recovered from both cementum and pulp of the same modern teeth. ^[58] They wish to apply this method also to historical and relatively ancient human teeth.