In 1.3 Million Years, Our Solar System Will Contain Two Stars

March 25, 2018 

Original link:  http://www.thescinewsreporter.com/2018/03/in-13-million-years-our-solar-system.html 

 

The Sun is used to having plenty of personal space, given that its nearest stellar neighbor, the Alpha Centauri system, is located about four light years away. While that's not very distant in cosmic terms, it's wide enough for our solar system to not be influenced by these alien stars.

But in about 1.3 million years, a star named Gliese 710, which is about 60 percent as massive as the Sun, is projected to interrupt the Sun's hermitude by crashing right on through the far-flung reaches of the solar system. While astronomers have been aware of this stellar meetup for years, new observations from the European Space Agency's Gaia satellite, released on Thursday, have constrained the trajectory of Gliese 710's impending visit, and charted out nearly 100 other upcoming close encounters with wandering stars.

According to the Gaia team, Gliese 710 will swoop through the Oort cloud, a vast shell of icy debris at the outer limits of the solar system, at a distance of roughly 90 light days, or 1.4 trillion miles. To put that into perspective, the star will be about 16,000 times farther from the Sun than Earth.

That may sound like a good stretch of space, but it is well within the boundaries of the Sun's domain. During the encounter, Gliese 710 will shine nearly three times brighter in Earth's skies than Mars. It could also spitball comets and ice worlds from the distant reaches of the solar system toward Earth, increasing the likelihood of deadly impacts.

Of course, we have over one million years to prepare for this disruptive passerby, but it's worth noting that it is far from the only star Gaia has identified as a potential trouble-maker.

Gaia, launched in 2013, has calculated the positions, magnitudes, parallaxes, and proper motions of millions of stars during its quest to create the most precise catalogue of the Milky Way's stellar population. Using this enormous dataset, scientists have plotted out the trajectories of 300,000 stars over the next five million years, and discovered that 97 of them will breach a radius of 93 trillion miles around the Sun.

Of those stars, 16 will travel within 37 trillion miles around the Sun, which is the rough distance at which stars begin to impact the solar system (though the extent to which they cause a ruckus depends on their mass and velocity).

It won't be the first time the Sun has had its personal space invaded by a stellar tourist. Only 70,000 years ago, around the time early humans were suffering from major volcano-induced endangerment, a dwarf star checked out the scene in the Oort cloud. Some scientists have even suggested that repeated encounters with nearby "death stars" are responsible for the cycle of mass extinctions on Earth, though the theory is controversial.

It goes to show that even the Sun has to deal with uninvited guests dropping by and causing mayhem. But now, thanks to Gaia, at least we can get an early heads-up to prepare for these otherworldly encounters.

DNA methylation

From Wikipedia, the free encyclopedia

Representation of a DNA molecule that is methylated. The two white spheres represent methyl groups. They are bound to two cytosine nucleotide molecules that make up the DNA sequence.

DNA methylation is a process by which methyl groups are added to the DNA molecule. Methylation can change the activity of a DNA segment without changing the sequence. When located in a gene promoter, DNA methylation typically acts to repress gene transcription. DNA methylation is essential for normal development and is associated with a number of key processes including genomic imprinting, X-chromosome inactivation, repression of transposable elements, aging and carcinogenesis.

Two of DNA's four bases, cytosine and adenine, can be methylated. Cytosine methylation is widespread in both eukaryotes and prokaryotes, even though the rate of cytosine DNA methylation can differ greatly between species: 14% of cytosines are methylated in Arabidopsis thaliana, 8% in Physarum,^[1] 4% in Mus musculus, 2.3% in Escherichia coli, 0.03% in Drosophila, 0.006% in Dictyostelium^[2] and virtually none (< 0.0002%) in Caenorhabditis^[3] or yeast species such as S. cerevisiae and S. pombe (but not N. crassa).^[4][5] Adenine methylation has been observed in bacterial, plant and recently in mammalian DNA,^[6][7] but has received considerably less attention.

Methylation of cytosine to form 5-methylcytosine occurs at the same 5 position on the pyrimidine ring where the DNA base thymine's methyl group is located; the same position distinguishes thymine from the analogous RNA base uracil, which has no methyl group. Spontaneous deamination of 5-methylcytosine converts it to thymine. This results in a T:G mismatch. Repair mechanisms then correct it back to the original C:G pair; alternatively, they may substitute G for A, turning the original C:G pair into an T:A pair, effectively changing a base and introducing a mutation. This misincorporated base will not be corrected during DNA replication as thymine is a DNA base. If the mismatch is not repaired and the cell enters the cell cycle the strand carrying the T will be complemented by an A in one of the daughter cells, such that the mutation becomes permanent. The near-universal replacement of uracil by thymine in DNA, but not RNA, may have evolved as an error-control mechanism, to facilitate the removal of uracils generated by the spontaneous deamination of cytosine.^[8] DNA methylation as well as many of its contemporary DNA methyltransferases has been thought to evolve from early world primitive RNA methylation activity and is supported by several lines of evidence.^[9]

In plants and other organisms, DNA methylation is found in three different sequence contexts: CG (or CpG), CHG or CHH (where H correspond to A, T or C). In mammals however, DNA methylation is almost exclusively found in CpG dinucleotides, with the cytosines on both strands being usually methylated. Non-CpG methylation can however be observed in embryonic stem cells,^[10][11][12] and has also been indicated in neural development.^[13] Furthermore, non-CpG methylation has also been observed in hematopoietic progenitor cells, and it occurred mainly in a CpApC sequence context.^[14]

Conserved function of DNA methylation

Typical DNA methylation landscape in mammals

The DNA methylation landscape of vertebrates is very particular compared to other organisms. In vertebrates, around 60–80% of CpG are methylated in somatic cells^[15] and DNA methylation appears as a default state that has to be specifically excluded from defined locations.^[16][17] By contrast, the genome of most plants, invertebrates, fungi or protists show “mosaic” methylation patterns, where only specific genomic elements are targeted, and they are characterized by the alternation of methylated and unmethylated domains.^[4][18]

High CpG methylation in mammalian genomes has an evolutionary cost because it increases the frequency of spontaneous mutations. Loss of amino-groups occurs with a high frequency for cytosines, with different consequences depending on their methylation. Methylated C residues spontaneously deaminate to form T residues over time; hence CpG dinucleotides steadily deaminate to TpG dinucleotides, which is evidenced by the under-representation of CpG dinucleotides in the human genome (they occur at only 21% of the expected frequency).^[19] (On the other hand, spontaneous deamination of unmethylated C residues gives rise to U residues, a change that is quickly recognized and repaired by the cell.)

CpG islands

In mammals, the only exception for this global CpG depletion resides in a specific category of GC- and CpG-rich sequences termed CpG islands that are generally unmethylated and therefore retained the expected CpG content.^[20] CpG islands are usually defined as regions with 1) a length greater than 200bp, 2) a G+C content greater than 50%, 3) a ratio of observed to expected CpG greater than 0.6, although other definitions are sometimes used.^[21] Excluding repeated sequences, there are around 25,000 CpG islands in the human genome, 75% of which being less than 850bp long.^[22] They are major regulatory units and around 50% of CpG islands are located in gene promoter regions, while another 25% lie in gene bodies, often serving as alternative promoters. Reciprocally, around 60-70% of human genes have a CpG island in their promoter region.^[23][24] The majority of CpG islands are constitutively unmethylated and enriched for permissive chromatin modification such as H3K4 methylation. In somatic tissues, only 10% of CpG islands are methylated, the majority of them being located in intergenic and intragenic regions.

Repression of CpG-dense promoters

DNA methylation was probably present at some extent in very early eukaryote ancestors. In virtually every organism analyzed, methylation in promoter regions correlates negatively with gene expression.^[4][25] CpG-dense promoters of actively transcribed genes are never methylated, but reciprocally transcriptionally silent genes do not necessarily carry a methylated promoter. In mouse and human, around 60–70% of genes have a CpG island in their promoter region and most of these CpG islands remain unmethylated independently of the transcriptional activity of the gene, in both differentiated and undifferentiated cell types.^[26][27] Of note, whereas DNA methylation of CpG islands is unambiguously linked with transcriptional repression, the function of DNA methylation in CG-poor promoters remains unclear; albeit there is little evidence that it could be functionally relevant.^[28]

DNA methylation may affect the transcription of genes in two ways. First, the methylation of DNA itself may physically impede the binding of transcriptional proteins to the gene,^[29] and second, and likely more important, methylated DNA may be bound by proteins known as methyl-CpG-binding domain proteins (MBDs). MBD proteins then recruit additional proteins to the locus, such as histone deacetylases and other chromatin remodeling proteins that can modify histones, thereby forming compact, inactive chromatin, termed heterochromatin. This link between DNA methylation and chromatin structure is very important. In particular, loss of methyl-CpG-binding protein 2 (MeCP2) has been implicated in Rett syndrome; and methyl-CpG-binding domain protein 2 (MBD2) mediates the transcriptional silencing of hypermethylated genes in cancer.

Repression of transposable elements

DNA methylation is a powerful transcriptional repressor, at least in CpG dense contexts. Transcriptional repression of protein-coding genes appears essentially limited to very specific classes of genes that need to be silent permanently and in almost all tissues. While DNA methylation does not have the flexibility required for the fine-tuning of gene regulation, its stability is perfect to ensure the permanent silencing of transposable elements. Transposon control is one the most ancient function of DNA methylation that is shared by animals, plants and multiple protists.^[30] It is even suggested that DNA methylation evolved precisely for this purpose.^[31]

Methylation of the gene body of highly transcribed genes

A function that appears even more conserved than transposon silencing is positively correlated with gene expression. In almost all species where DNA methylation is present, DNA methylation is especially enriched in the body of highly transcribed genes.^[4][25] The function of gene body methylation is not well understood. A body of evidence suggests that it could regulate splicing^[32] and suppress the activity of intragenic transcriptional units (cryptic promoters or transposable elements).^[33] Gene-body methylation appears closely tied to H3K36 methylation. In yeast and mammals, H3K36 methylation is highly enriched in the body of highly transcribed genes. In yeast at least, H3K36me3 recruits enzymes such as histone deacetylases to condense chromatin and prevent the activation of cryptic start sites.^[34] In mammals, DNMT3a and DNMT3b PWWP domain binds to H3K36me3 and the two enzymes are recruited to the body of actively transcribed genes.

In mammals

Dynamic of DNA methylation during mouse embryonic development. E3.5-E6, etc., refer to days after fertilization. PGC: primordial germ cells

During embryonic development

DNA methylation patterns are largely erased and then re-established between generations in mammals. Almost all of the methylations from the parents are erased, first during gametogenesis, and again in early embryogenesis, with demethylation and remethylation occurring each time. Demethylation in early embryogenesis occurs in the preimplantation period in two stages – initially in the zygote, then during the first few embryonic replication cycles of morula and blastula. A wave of methylation then takes place during the implantation stage of the embryo, with CpG islands protected from methylation. This results in global repression and allows housekeeping genes to be expressed in all cells. In the post-implantation stage, methylation patterns are stage- and tissue-specific, with changes that would define each individual cell type lasting stably over a long period.^[35]

Whereas DNA methylation is not necessary per se for transcriptional silencing, it is thought nonetheless to represent a “locked” state that definitely inactivates transcription. In particular, DNA methylation appears critical for the maintenance of mono-allelic silencing in the context of genomic imprinting and X chromosome inactivation.^[36][37] In these cases, expressed and silent alleles differ by their methylation status, and loss of DNA methylation results in loss of imprinting and re-expression of Xist in somatic cells. During embryonic development, few genes change their methylation status, at the important exception of many genes specifically expressed in the germline.^[38] DNA methylation appears absolutely required in differentiated cells, as knockout of any of the three competent DNA methyltransferase results in embryonic or post-partum lethality. By contrast, DNA methylation is dispensable in undifferentiated cell types, such as the inner cell mass of the blastocyst, primordial germ cells or embryonic stem cells. Since DNA methylation appears to directly regulate only a limited number of genes, how precisely DNA methylation absence causes the death of differentiated cells remain an open question.

Due to the phenomenon of genomic imprinting, maternal and paternal genomes are differentially marked and must be properly reprogrammed every time they pass through the germline. Therefore, during gametogenesis, primordial germ cells must have their original biparental DNA methylation patterns erased and re-established based on the sex of the transmitting parent. After fertilization the paternal and maternal genomes are once again demethylated and remethylated (except for differentially methylated regions associated with imprinted genes). This reprogramming is likely required for totipotency of the newly formed embryo and erasure of acquired epigenetic changes.^[39]

In cancer

In many disease processes, such as cancer, gene promoter CpG islands acquire abnormal hypermethylation, which results in transcriptional silencing that can be inherited by daughter cells following cell division. Alterations of DNA methylation have been recognized as an important component of cancer development. Hypomethylation, in general, arises earlier and is linked to chromosomal instability and loss of imprinting, whereas hypermethylation is associated with promoters and can arise secondary to gene (oncogene suppressor) silencing, but might be a target for epigenetic therapy.^[40]

Global hypomethylation has also been implicated in the development and progression of cancer through different mechanisms.^[41] Typically, there is hypermethylation of tumor suppressor genes and hypomethylation of oncogenes.^[42]

Generally, in progression to cancer, hundreds of genes are silenced or activated. Although silencing of some genes in cancers occurs by mutation, a large proportion of carcinogenic gene silencing is a result of altered DNA methylation. DNA methylation causing silencing in cancer typically occurs at multiple CpG sites in the CpG islands that are present in the promoters of protein coding genes.

Altered expressions of microRNAs also silence or activate many genes in progression to cancer (see microRNAs in cancer). Altered microRNA expression occurs through hyper/hypo-methylation of CpG sites in CpG islands in promoters controlling transcription of the microRNAs.

Silencing of DNA repair genes through methylation of CpG islands in their promoters appears to be especially important in progression to cancer.

In atherosclerosis

Epigenetic modifications such as DNA methylation have been implicated in cardiovascular disease, including atherosclerosis. In animal models of atherosclerosis, vascular tissue as well as blood cells such as mononuclear blood cells exhibit global hypomethylation with gene-specific areas of hypermethylation. DNA methylation polymorphisms may be used as an early biomarker of atherosclerosis since they are present before lesions are observed, which may provide an early tool for detection and risk prevention.^[43]

Two of the cell types targeted for DNA methylation polymorphisms are monocytes and lymphocytes, which experience an overall hypomethylation. One proposed mechanism behind this global hypomethylation is elevated homocysteine levels causing hyperhomocysteinemia, a known risk factor for cardiovascular disease. High plasma levels of homocysteine inhibit DNA methyltransferases, which causes hypomethylation. Hypomethylation of DNA affects gene that alter smooth muscle cell proliferation, cause endothelial cell dysfunction, and increase inflammatory mediators, all of which are critical in forming atherosclerotic lesions.^[44] High levels of homocysteine also result in hypermethylation of CpG islands in the promoter region of the estrogen receptor alpha (ERα) gene, causing its down regulation.^[45] ERα protects against atherosclerosis due to its action as a growth suppressor, causing the smooth muscle cells to remain in a quiescent state.^[46] Hypermethylation of the ERα promoter thus allows intimal smooth muscle cells to proliferate excessively and contribute to the development of the atherosclerotic lesion.^[47]

Another gene that experiences a change in methylation status in atherosclerosis is the monocarboxylate transporter (MCT3), which produces a protein responsible for the transport of lactate and other ketone bodies out of many cell types, including vascular smooth muscle cells. In atherosclerosis patients, there is an increase in methylation of the CpG islands in exon 2, which decreases MCT3 protein expression. The down regulation of MCT3 impairs lactate transport, and significantly increases smooth muscle cell proliferation, which further contributes to the atherosclerotic lesion. An ex vivo experiment using the demethylating agent Decitabine (5-aza-2 -deoxycytidine) was shown to induce MCT3 expression in a dose dependant manner, as all hypermethylated sites in the exon 2 CpG island became demethylated after treatment. This may serve as a novel therapeutic agent to treat atherosclerosis, although no human studies have been conducted thus far.^[48]

In aging

In humans and other mammals, DNA methylation levels can be used to accurately estimate the age of tissues and cell types, forming an accurate epigenetic clock.^[49]

A longitudinal study of twin children showed that, between the ages of 5 and 10, there was divergence of methylation patterns due to environmental rather than genetic influences.^[50] There is a global loss of DNA methylation during aging.^[42]

In a study that analyzed the complete DNA methylomes of CD4⁺ T cells in a newborn, a 26 years old individual and a 103 years old individual was observed that the loss of methylation is proportional to age. Hypomethylated CpGs observed in the centenarian DNAs compared with the neonates covered all genomic compartments (promoters, intergenic, intronic and exonic regions).^[51] However, some genes become hypermethylated with age, including genes for the estrogen receptor, p16, and insulin-like growth factor 2.^[42]

In exercise

High intensity exercise has been shown to result in reduced DNA methylation in skeletal muscle.^[52] Promoter methylation of PGC-1α and PDK4 were immediately reduced after high intensity exercise, whereas PPAR-γ methylation was not reduced until three hours after exercise.^[52] By contrast, six months of exercise in previously sedentary middle-age men resulted in increased methylation in adipose tissue.^[53] One study showed a possible increase in global genomic DNA methylation of white blood cells with more physical activity in non-Hispanics.^[54]

In B-cell differentiation

A study that investigated the methylome of B cells along their differentiation cycle, using whole-genome bisulfite sequencing (WGBS), showed that there is a hypomethylation from the earliest stages to the most differentiated stages. The largest methylation difference is between the stages of germinal center B cells and memory B cells. Furthermore, this study showed that there is a similarity between B cell tumors and long-lived B cells in their DNA methylation signatures.^[14]

In the brain

Research has suggested that long-term memory storage in humans may be regulated by DNA methylation.^[55][56]

DNA methyltransferases (in mammals)

Possible pathways of cytosine methylation and demethylation. Abbreviations: S-Adenosyl-L-homocysteine (SAH), S-adenosyl-L-methionine (SAM), DNA methyltransferase (DNA MTase), Uracil-DNA glycosylase (UNG)

In mammalian cells, DNA methylation occurs mainly at the C5 position of CpG dinucleotides and is carried out by two general classes of enzymatic activities – maintenance methylation and de novo methylation.^[57]

Maintenance methylation activity is necessary to preserve DNA methylation after every cellular DNA replication cycle. Without the DNA methyltransferase (DNMT), the replication machinery itself would produce daughter strands that are unmethylated and, over time, would lead to passive demethylation. DNMT1 is the proposed maintenance methyltransferase that is responsible for copying DNA methylation patterns to the daughter strands during DNA replication. Mouse models with both copies of DNMT1 deleted are embryonic lethal at approximately day 9, due to the requirement of DNMT1 activity for development in mammalian cells.

It is thought that DNMT3a and DNMT3b are the de novo methyltransferases that set up DNA methylation patterns early in development. DNMT3L is a protein that is homologous to the other DNMT3s but has no catalytic activity. Instead, DNMT3L assists the de novo methyltransferases by increasing their ability to bind to DNA and stimulating their activity. Finally, DNMT2 (TRDMT1) has been identified as a DNA methyltransferase homolog, containing all 10 sequence motifs common to all DNA methyltransferases; however, DNMT2 (TRDMT1) does not methylate DNA but instead methylates cytosine-38 in the anticodon loop of aspartic acid transfer RNA.^[58]

Since many tumor suppressor genes are silenced by DNA methylation during carcinogenesis, there have been attempts to re-express these genes by inhibiting the DNMTs. 5-Aza-2'-deoxycytidine (decitabine) is a nucleoside analog that inhibits DNMTs by trapping them in a covalent complex on DNA by preventing the β-elimination step of catalysis, thus resulting in the enzymes' degradation. However, for decitabine to be active, it must be incorporated into the genome of the cell, which can cause mutations in the daughter cells if the cell does not die. In addition, decitabine is toxic to the bone marrow, which limits the size of its therapeutic window. These pitfalls have led to the development of antisense RNA therapies that target the DNMTs by degrading their mRNAs and preventing their translation. However, it is currently unclear whether targeting DNMT1 alone is sufficient to reactivate tumor suppressor genes silenced by DNA methylation.

In plants

Significant progress has been made in understanding DNA methylation in the model plant Arabidopsis thaliana. DNA methylation in plants differs from that of mammals: while DNA methylation in mammals mainly occurs on the cytosine nucleotide in a CpG site, in plants the cytosine can be methylated at CpG, CpHpG, and CpHpH sites, where H represents any nucleotide but not guanine. Overall, Arabidopsis DNA is highly methylated, mass spectrometry analysis estimated 14% of cytosines to be modified.^[5]

The principal Arabidopsis DNA methyltransferase enzymes, which transfer and covalently attach methyl groups onto DNA, are DRM2, MET1, and CMT3. Both the DRM2 and MET1 proteins share significant homology to the mammalian methyltransferases DNMT3 and DNMT1, respectively, whereas the CMT3 protein is unique to the plant kingdom. There are currently two classes of DNA methyltransferases: 1) the de novo class, or enzymes that create new methylation marks on the DNA; and 2) a maintenance class that recognizes the methylation marks on the parental strand of DNA and transfers new methylation to the daughters strands after DNA replication. DRM2 is the only enzyme that has been implicated as a de novo DNA methyltransferase. DRM2 has also been shown, along with MET1 and CMT3 to be involved in maintaining methylation marks through DNA replication.^[59] Other DNA methyltransferases are expressed in plants but have no known function.

It is not clear how the cell determines the locations of de novo DNA methylation, but evidence suggests that, for many (though not all) locations, RNA-directed DNA methylation (RdDM) is involved. In RdDM, specific RNA transcripts are produced from a genomic DNA template, and this RNA forms secondary structures called double-stranded RNA molecules.^[60] The double-stranded RNAs, through either the small interfering RNA (siRNA) or microRNA (miRNA) pathways direct de-novo DNA methylation of the original genomic location that produced the RNA.^[60] This sort of mechanism is thought to be important in cellular defense against RNA viruses and/or transposons, both of which often form a double-stranded RNA that can be mutagenic to the host genome. By methylating their genomic locations, through an as yet poorly understood mechanism, they are shut off and are no longer active in the cell, protecting the genome from their mutagenic effect. Recently, it was described that methylation of the DNA is the main determinant of embryogenic cultures formation from explants in woody plants and is regarded the main mechanism that explains the poor response of mature explants to somatic embryogenesis in the plants (Isah 2016).

In insects

Functional DNA methylation has been discovered in Honey Bees.^[61][62] DNA methylation marks are mainly on the gene body, and current opinions on the function of DNA methylation is gene regulation via alternative splicing ^[63]

DNA methylation levels in Drosophila melanogaster are nearly undetectable.^[64] Sensitive methods applied to Drosophila DNA Suggest levels in the range of 0.1–0.3% of total cytosine.^[65] This low level of methylation ^[66] appears to reside in genomic sequence patterns that are very different from patterns seen in humans, or in other animal or plant species to date. Genomic methylation in D. melanogaster was found at specific short motifs (concentrated in specific 5-base sequence motifs that are CA- and CT-rich but depleted of guanine) and is independent of DNMT2 activity. Further, highly sensitive mass spectrometry approaches,^[67] have now demonstrated the presence of low (0.07%) but significant levels of adenine methylation during the earliest stages of Drosophila embryogenesis.

In fungi

Many fungi have low levels (0.1 to 0.5%) of cytosine methylation, whereas other fungi have as much as 5% of the genome methylated.^[68] This value seems to vary both among species and among isolates of the same species.^[69] There is also evidence that DNA methylation may be involved in state-specific control of gene expression in fungi.^{[citation needed]} However, at a detection limit of 250 attomoles by using ultra-high sensitive mass spectrometry DNA methylation was not confirmed in single cellular yeast species such as Saccharomyces cerevisiae or Schizosaccharomyces pombe, indicating that yeasts do not possess this DNA modification.^[5]

Although brewers' yeast (Saccharomyces), fission yeast (Schizosaccharomyces), and Aspergillus flavus^[70] have no detectable DNA methylation, the model filamentous fungus Neurospora crassa has a well-characterized methylation system.^[71] Several genes control methylation in Neurospora and mutation of the DNA methyl transferase, dim-2, eliminates all DNA methylation but does not affect growth or sexual reproduction. While the Neurospora genome has very little repeated DNA, half of the methylation occurs in repeated DNA including transposon relics and centromeric DNA. The ability to evaluate other important phenomena in a DNA methylase-deficient genetic background makes Neurospora an important system in which to study DNA methylation.

In lower eukaryotes

DNA methylation is largely absent from Dictyostelium discoidium^[72] where it appears to occur at about 0.006% of cytosines.^[2] In contrast, DNA methylation is widely distributed in Physarum polycephalum ^[73] where 5-methylcytosine makes up as much as 8% of total cytosine^[1]

In bacteria

Adenine or cytosine methylation is part of the restriction modification system of many bacteria, in which specific DNA sequences are methylated periodically throughout the genome. A methylase is the enzyme that recognizes a specific sequence and methylates one of the bases in or near that sequence. Foreign DNAs (which are not methylated in this manner) that are introduced into the cell are degraded by sequence-specific restriction enzymes and cleaved. Bacterial genomic DNA is not recognized by these restriction enzymes. The methylation of native DNA acts as a sort of primitive immune system, allowing the bacteria to protect themselves from infection by bacteriophage.

E. coli DNA adenine methyltransferase (Dam) is an enzyme of ~32 kDa that does not belong to a restriction/modification system. The target recognition sequence for E. coli Dam is GATC, as the methylation occurs at the N6 position of the adenine in this sequence (G meATC). The three base pairs flanking each side of this site also influence DNA–Dam binding. Dam plays several key roles in bacterial processes, including mismatch repair, the timing of DNA replication, and gene expression. As a result of DNA replication, the status of GATC sites in the E. coli genome changes from fully methylated to hemimethylated. This is because adenine introduced into the new DNA strand is unmethylated. Re-methylation occurs within two to four seconds, during which time replication errors in the new strand are repaired. Methylation, or its absence, is the marker that allows the repair apparatus of the cell to differentiate between the template and nascent strands. It has been shown that altering Dam activity in bacteria results in increased spontaneous mutation rate. Bacterial viability is compromised in dam mutants that also lack certain other DNA repair enzymes, providing further evidence for the role of Dam in DNA repair.

One region of the DNA that keeps its hemimethylated status for longer is the origin of replication, which has an abundance of GATC sites. This is central to the bacterial mechanism for timing DNA replication. SeqA binds to the origin of replication, sequestering it and thus preventing methylation. Because hemimethylated origins of replication are inactive, this mechanism limits DNA replication to once per cell cycle.

Expression of certain genes, for example those coding for pilus expression in E. coli, is regulated by the methylation of GATC sites in the promoter region of the gene operon. The cells' environmental conditions just after DNA replication determine whether Dam is blocked from methylating a region proximal to or distal from the promoter region. Once the pattern of methylation has been created, the pilus gene transcription is locked in the on or off position until the DNA is again replicated. In E. coli, these pilus operons have important roles in virulence in urinary tract infections. It has been proposed^{[by whom?]} that inhibitors of Dam may function as antibiotics.

On the other hand, DNA cytosine methylase targets CCAGG and CCTGG sites to methylate cytosine at the C5 position (C meC(A/T) GG). The other methylase enzyme, EcoKI, causes methylation of adenines in the sequences AAC(N₆)GTGC and GCAC(N₆)GTT.

Molecular cloning

Most strains used by molecular biologists are derivatives of E. coli K-12, and possess both Dam and Dcm, but there are commercially available strains that are dam-/dcm- (lack of activity of either methylase). In fact, it is possible to unmethylate the DNA extracted from dam+/dcm+ strains by transforming it into dam-/dcm- strains. This would help digest sequences that are not being recognized by methylation-sensitive restriction enzymes.^[74][75]

The restriction enzyme DpnI can recognize 5'-GmeATC-3' sites and digest the methylated DNA. Being such a short motif, it occurs frequently in sequences by chance, and as such its primary use for researchers is to degrade template DNA following PCRs (PCR products lack methylation, as no methylases are present in the reaction). Similarly, some commercially available restriction enzymes are sensitive to methylation at their cognate restriction sites, and must as mentioned previously be used on DNA passed through a dam-/dcm- strain to allow cutting.

Detection

DNA methylation can be detected by the following assays currently used in scientific research:^[76]

• Mass spectrometry is a very sensitive and reliable analytical method to detect DNA methylation. MS in general is however not informative about the sequence context of the methylation, thus limited in studying the function of this DNA modification.
• Methylation-Specific PCR (MSP), which is based on a chemical reaction of sodium bisulfite with DNA that converts unmethylated cytosines of CpG dinucleotides to uracil or UpG, followed by traditional PCR.^[77] However, methylated cytosines will not be converted in this process, and primers are designed to overlap the CpG site of interest, which allows one to determine methylation status as methylated or unmethylated.
• Whole genome bisulfite sequencing, also known as BS-Seq, which is a high-throughput genome-wide analysis of DNA methylation. It is based on aforementioned sodium bisulfite conversion of genomic DNA, which is then sequenced on a Next-generation sequencing platform. The sequences obtained are then re-aligned to the reference genome to determine methylation states of CpG dinucleotides based on mismatches resulting from the conversion of unmethylated cytosines into uracil.
• Reduced representation bisulfite sequencing, also known as RRBS knows several working protocols. The first RRBS protocol was called RRBS and aims for around 10% of the methylome, a reference genome is needed. Later came more protocols that were able to sequence a smaller portion of the genome and higher sample multiplexing. EpiGBS was the first protocol were you could multiplex 96 sample in one lane of Illumina sequencing and were a reference genome was not longer needed. A de novo reference construction from the Watson and Crick reads made population screening of SNP's and SMP's simultaneously a fact.
• The HELP assay, which is based on restriction enzymes' differential ability to recognize and cleave methylated and unmethylated CpG DNA sites.
• GLAD-PCR assay, which is based on new type of enzymes – site-specific methyl-directed DNA endonucleases, which hydrolyze only methylated DNA.
• ChIP-on-chip assays, which is based on the ability of commercially prepared antibodies to bind to DNA methylation-associated proteins like MeCP2.
• Restriction landmark genomic scanning, a complicated and now rarely used assay based upon restriction enzymes' differential recognition of methylated and unmethylated CpG sites; the assay is similar in concept to the HELP assay.
• Methylated DNA immunoprecipitation (MeDIP), analogous to chromatin immunoprecipitation, immunoprecipitation is used to isolate methylated DNA fragments for input into DNA detection methods such as DNA microarrays (MeDIP-chip) or DNA sequencing (MeDIP-seq).
• Pyrosequencing of bisulfite treated DNA. This is sequencing of an amplicon made by a normal forward primer but a biotinylated reverse primer to PCR the gene of choice. The Pyrosequencer then analyses the sample by denaturing the DNA and adding one nucleotide at a time to the mix according to a sequence given by the user. If there is a mis-match, it is recorded and the percentage of DNA for which the mis-match is present is noted. This gives the user a percentage methylation per CpG island.
• Molecular break light assay for DNA adenine methyltransferase activity – an assay that relies on the specificity of the restriction enzyme DpnI for fully methylated (adenine methylation) GATC sites in an oligonucleotide labeled with a fluorophore and quencher. The adenine methyltransferase methylates the oligonucleotide making it a substrate for DpnI. Cutting of the oligonucleotide by DpnI gives rise to a fluorescence increase.^[78][79]
• Methyl Sensitive Southern Blotting is similar to the HELP assay, although uses Southern blotting techniques to probe gene-specific differences in methylation using restriction digests. This technique is used to evaluate local methylation near the binding site for the probe.
• MethylCpG Binding Proteins (MBPs) and fusion proteins containing just the Methyl Binding Domain (MBD) are used to separate native DNA into methylated and unmethylated fractions. The percentage methylation of individual CpG islands can be determined by quantifying the amount of the target in each fraction.^[80] Extremely sensitive detection can be achieved in FFPE tissues with abscription-based detection.
• High Resolution Melt Analysis (HRM or HRMA), is a post-PCR analytical technique. The target DNA is treated with sodium bisulfite, which chemically converts unmethylated cytosines into uracils, while methylated cytosines are preserved. PCR amplification is then carried out with primers designed to amplify both methylated and unmethylated templates. After this amplification, highly methylated DNA sequences contain a higher number of CpG sites compared to unmethylated templates, which results in a different melting temperature that can be used in quantitative methylation detection.^[81][82]
• Ancient DNA methylation reconstruction, a method to reconstruct high-resolution DNA methylation from ancient DNA samples. The method is based on the natural degradation processes that occur in ancient DNA: with time, methylated cytosines are degraded into thymines, whereas unmethylated cytosines are degraded into uracils. This asymmetry in degradation signals was used to reconstruct the full methylation maps of the Neanderthal and the Denisovan ^[83]

Differentially methylated regions (DMRs)

Differentially methylated regions, are genomic regions with different methylation statuses among multiple samples (tissues, cells, individuals or others), are regarded as possible functional regions involved in gene transcriptional regulation. The identification of DMRs among multiple tissues (T-DMRs) provides a comprehensive survey of epigenetic differences among human tissues.^[84] For example, these methylated regions that are unique to a particular tissue allow individuals to differentiate between tissue type, such as semen and vaginal fluid. Current research conducted by Lee et al., showed DACT1 and USP49 positively identified semen by examining T-DMRs.^[85] DMRs between cancer and normal samples (C-DMRs) demonstrate the aberrant methylation in cancers.^[86] It is well known that DNA methylation is associated with cell differentiation and proliferation.^[87] Many DMRs have been found in the development stages (D-DMRs) ^[88] and in the reprogrammed progress (R-DMRs).^[89] In addition, there are intra-individual DMRs (Intra-DMRs) with longitudinal changes in global DNA methylation along with the increase of age in a given individual.^[90] There are also inter-individual DMRs (Inter-DMRs) with different methylation patterns among multiple individuals.^[91]

QDMR (Quantitative Differentially Methylated Regions) is a quantitative approach to quantify methylation difference and identify DMRs from genome-wide methylation profiles by adapting Shannon entropy <http://bioinfo.hrbmu.edu.cn/qdmr>. The platform-free and species-free nature of QDMR makes it potentially applicable to various methylation data. This approach provides an effective tool for the high-throughput identification of the functional regions involved in epigenetic regulation. QDMR can be used as an effective tool for the quantification of methylation difference and identification of DMRs across multiple samples.^[92]

Gene-set analysis (a.k.a. pathway analysis; usually performed tools such as DAVID, GoSeq or GSEA) has been shown to be severely biased when applied to high-throughput methylation data (e.g. MeDIP-seq, MeDIP-ChIP, HELP-seq etc.), and a wide range of studies have thus mistakenly reported hyper-methylation of genes related to development and differentiation; it has been suggested that this can be corrected using sample label permutations or using a statistical model to control for differences in the numberes of CpG probes / CpG sites that target each gene.^[93]

DNA methylation marks

DNA methylation marks, are genomic regions with specific methylation pattern in a specific biological state such as tissue, cell type, individual), are regarded as possible functional regions involved in gene transcriptional regulation. Although various human cell types may have the same genome, these cells have different methylomes. The systematic identification and characterization of methylation marks across cell types are crucial to understanding the complex regulatory network for cell fate determination. Hongbo Liu et al. proposed an entropy-based framework termed SMART to integrate the whole genome bisulfite sequencing methylomes across 42 human tissues/cells and identified 757,887 genome segments.^[94] Nearly 75% of the segments showed uniform methylation across all cell types. From the remaining 25% of the segments, they identified cell type-specific hypo/hypermethylation marks that were specifically hypo/hypermethylated in a minority of cell types using a statistical approach and presented an atlas of the human methylation marks. Further analysis revealed that the cell type-specific hypomethylation marks were enriched through H3K27ac and transcription factor binding sites in cell type-specific manner. In particular, they observed that the cell type-specific hypomethylation marks are associated with the cell type-specific super-enhancers that drive the expression of cell identity genes. This framework provides a complementary, functional annotation of the human genome and helps to elucidate the critical features and functions of cell type-specific hypomethylation.

The entropy-based Specific Methylation Analysis and Report Tool, termed "SMART", which focuses on integrating a large number of DNA methylomes for the de novo identification of cell type-specific methylation marks. The latest version of SMART is focused on three main functions including de novo identification of differentially methylated regions (DMRs) by genome segmentation, identification of DMRs from predefined regions of interest, and identification of differentially methylated CpG sites. SMART is available at http://fame.edbc.org/smart/.

Computational prediction

DNA methylation can also be detected by computational models through sophisticated algorithms and methods. Computational models can facilitate the global profiling of DNA methylation across chromosomes, and often such models are faster and cheaper to perform than biological assays. Such up-to-date computational models include Bhasin, et al.,^[95] Bock, et al.,^[96] and Zheng, et al.^{[97] [98]} Together with biological assay, these methods greatly facilitate the DNA methylation analysis.

Epigenetic clock

From Wikipedia, the free encyclopedia

An epigenetic clock is a type of a molecular age estimation method based on DNA methylation levels. Pre-eminent examples for epigenetic clocks are Horvath's clock^[1][2][3][4], which applies to all human tissues/cells, and Hannum's clock ^[5], which applies to blood.

History

The strong effects of age on DNA methylation levels have been known since the late 1960s.^[6] A vast literature describes sets of CpGs whose DNA methylation levels correlate with age, e.g.^{[7][8][9][10][11]}. The first robust demonstration that DNA methylation levels in saliva could generate accurate age predictors was published by a UCLA team including Steve Horvath in 2011 (Bocklandt et al 2011) ^[12]. The labs of Trey Ideker and Kang Zhang at the University of California San Diego published the Hannum epigenetic clock (Hannum 2013) ^[5], which consisted of 71 markers that accurately estimate age based on blood methylation levels. The first multi-tissue epigenetic clock, Horvath's epigenetic clock, was developed by Steve Horvath, a professor of human genetics and of biostatistics at UCLA (Horvath 2013)^[1][3]. Horvath spent over 4 years collecting publicly available Illumina DNA methylation data and identifying suitable statistical methods.^[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 organism, i.e. 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 DNA methylation age measures the cumulative effect of an epigenetic maintenance system but details are unknown. The fact that DNA methylation age of blood predicts all-cause mortality in later life ^{[15][16][17][18]} strongly suggests that it relates to a process that causes aging.^[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 find many uses in biological research since age is 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,
• serving as surrogate markers for evaluating therapeutic interventions including rejuvenation approaches,
• studying developmental biology and cell differentiation,
• forensic applications, for example to estimate the age of a suspect based on blood left 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. Estimated age ("predicted age" in mathematical usage), also referred to as DNA methylation age, 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-cause mortality in later life even after adjusting for known risk factors ^[15][16] suggests that it relates to a process that causes aging. Similarly, markers of physical and mental fitness are associated with the epigenetic clock (lower abilities associated with age acceleration).^[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 due to disease.

Statistical approach

The basic approach is to form a weighted average of the 353 clock CpGs, which is then transformed to DNAm age 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, Horvath used a penalized regression model (Elastic net regularization) to regress a calibrated version of 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. 193 of the 353 CpGs correlate positively with age while the remaining 160 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 of estimated age is 3.6 years across a wide spectrum 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, buccal epithelium, colon, adipose, kidney, liver, lung, saliva, uterine cervix, epidermis, muscle) as well as in individual cell types such as CD4 T cells, CD14 monocytes, glial cells, neurons, immortalized B cells, mesenchymal stromal cells.^[1] However, accuracy depends to 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 p16INK4a in T cells is r=0.56.^[25] p16INK4a expression levels only relate to age in T cells, a type of white blood cells.^{[citation needed]} The microsatellite clock measures not chronological age but age in terms of elapsed cell divisions within a tissue.^{[citation needed]}

Applications of Horvath's clock

By contrasting DNA methylation age (estimated age) with chronological age, one can define measures of age acceleration. Age acceleration can be defined as the difference between DNA methylation age and chronological age. Alternatively, it can be defined as the residual that results from regressing DNAm 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 heritability (defined via Falconer's formula) of age acceleration of blood from older subjects is around 40% but it appears to be much higher in newborns.^[1] Similarly, the age acceleration of brain tissue (prefrontal cortex) was found to be 41% in older subjects.^[26] Genome-wide association studies of cerebellar age acceleration have identified several SNPs at a genomewide significance level.^[27][28] Gene and SNP sets found by genome-wide association analysis of epigenetic age acceleration exhibit significant overlap with those of Alzheimer’s disease, age-related macular degeneration, and Parkinson’s disease.^[27][28]

Female breast tissue is older than expected

DNAm age is higher than chronological age in female breast tissue that is adjacent to breast cancer tissue.^[1] Since normal tissue which is adjacent to other cancer types does not exhibit a similar age acceleration effect, this finding suggests that normal female breast tissue ages faster than other parts of the body.^[1] Similarly, normal breast tissue samples from women without cancer have been found to be substantially older than blood samples collected from the same women at the same time 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 acceleration and tumor morphology (grade/stage).^[1][2] On average, cancer tissues with mutated TP53 have a lower age 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 DNAm 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 acceleration in glioblastoma multiforme samples is highly significantly associated with certain mutations in H3F3A.^[1] One study suggests that the epigenetic age of blood tissue may be prognostic of lung cancer incidence.^[29]

Obesity and metabolic syndrome

The epigenetic clock was used to study the relationship between high body mass index (BMI) and the DNA methylation ages of human blood, liver, muscle and adipose tissue.^[30] 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 the good cholesterol HDL were associated with a lower epigenetic aging rate of blood PMID 28198702.

Trisomy 21 (Down syndrome)

Down Syndrome (DS) entails an increased risk of many chronic diseases that are typically 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).^[31]

Alzheimer's disease related neuropathology

Epigenetic age acceleration of the human prefrontal cortex was found to be correlated with several neuropathological measurements that play a role in Alzheimer's disease ^[26] Further, it was found to be associated with a decline in global cognitive functioning, and memory functioning among individuals with Alzheimer's disease.^[26] The epigenetic age of blood relates to cognitive functioning in the elderly.^[20] Overall, these results strongly suggest that the epigenetic clock lends itself for measuring the biological age of the brain.

Cerebellum ages slowly

It has been difficult to identify tissues that seem to evade aging due to the lack of biomarkers of tissue age that allow one to contrast compare the ages of different tissues. An application of epigenetic clock to 30 anatomic sites from six centenarians and younger subjects revealed that the cerebellum ages slowly: it is about 15 years younger than expected in a centenarian.^[32] This finding might explain why the cerebellum exhibits fewer neuropathological hallmarks of age related dementias compared to other brain regions. In younger subjects (e.g. younger than 70), brain regions and brain cells appear to have roughly the same age.^[1][32] Several SNPs and genes have been identified that relate to the epigenetic age of the cerebellum ^[27]

Huntington's disease

Huntington's disease has been found to increase the epigenetic aging rates of several human brain regions.^[33]

Centenarians age slowly

The offspring of semi-supercentenarians (subjects who reached an age of 105–109 years) have a lower epigenetic age than age-matched controls (age difference=5.1 years in blood) and centenarians are younger (8.6 years) than expected based on their chronological age.^[18]

HIV infection

Infection with the Human Immunodeficiency Virus-1 (HIV) is associated with clinical symptoms of accelerated aging, as evidenced by increased incidence and diversity of age-related illnesses at relatively young ages. 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 a significant age acceleration effect in brain (7.4 years) and blood (5.2 years) tissue due to HIV-1 infection.^[34] These results are consistent with an independent study that also found an age advancement of 5 years in blood of HIV patients and a strong effect of the HLA locus.^[35]

Parkinson's disease

A large-scale study suggests that the blood of Parkinson's disease subjects exhibits (relatively weak) accelerated aging effects.^[36]

Developmental disorder: syndrome X

Children with a very rare disorder known as syndrome X maintain the façade of persistent toddler-like features while aging from birth to adulthood. Since the physical development of these children is dramatically delayed, these children appear to be a toddler or at best a preschooler. According to an epigenetic clock analysis, blood tissue from syndrome X cases is not younger than expected.^[37]

Menopause accelerates epigenetic aging

The following results strongly suggest that the loss of female hormones resulting from menopause accelerates the epigenetic aging rate of blood and possibly that of other tissues.^[38] First, early menopause has been found to be associated with an increased epigenetic age acceleration of blood.^[38] 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 acceleration of buccal cells (but not of blood cells).^[38] Fourth, genetic markers that are associated with early menopause are also associated with increased epigenetic age acceleration in blood.^[38]

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 descriptions of the same phenomenon instigated by different sources, or if each of these is distinct, and how they are associated with epigenetic aging. Induction of replicative senescence (RS) and oncogene-induced senescence (OIS) were found to be accompanied by epigenetic aging of primary cells but senescence induced by DNA damage was not, even though RS and OIS activate the cellular DNA damage response pathway.^[39] These results highlight the independence of cellular senescence from epigenetic aging. Consistent with this, telomerase-immortalised cells continued to age (according to the epigenetic clock) without having been treated with any senescence inducers or DNA-damaging agents, re-affirming the independence of the process of epigenetic ageing from 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 ageing, as demonstrated by Baker et al., where removal of senescent cells slowed down aging.^[40] However, the epigenetic clock analysis of senescence suggests that cellular senescence is a state that cells are forced into as a result of external pressures such as DNA damage, ectopic oncogene expression and exhaustive proliferation of cells to replenish those eliminated by external/environmental factors.^[39] These senescent cells, in sufficient numbers, will probably cause the deterioration of tissues, which is interpreted as organism ageing. 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 if cells are not shunted into senescence by the external pressures described above, they would still continue to age. This is consistent with the fact that mice with naturally long telomeres still age and eventually die even though their telomere lengths are far longer than the critical limit, and they age prematurely when their telomeres are forcibly shortened, due to replicative senescence. Therefore, cellular senescence is a route by which cells exit prematurely from the natural course of cellular aging.^[39]

Effect of sex and race/ethnicity

Men age faster than women according to epigenetic age acceleration in blood, brain, saliva, and many other tissues. ^[41] 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.^[41] For example, the blood of Hispanics and the Tsimané ages more slowly than that of other populations which might explain the Hispanic mortality paradox.^[41]

Rejuvenation effect due to 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

Adult progeria also known as Werner syndrome is associated with epigenetic age acceleration in blood.^[42]

Biological mechanism behind the epigenetic clock

Despite the fact that biomarkers of ageing based on DNA methylation data have enabled accurate age estimates for any tissue across the entire life course, the precise biological mechanism behind the epigenetic clock is currently unknown^[43]. However, epigenetic biomarkers may help to address long-standing questions in many fields, including the central question: why do we age? 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 damages occur frequently including about 50 double-strand DNA breaks per cell cycle^[44] and about 10,000 oxidative damages per day (see DNA damage (naturally occurring)). During repair of double-strand breaks many epigenetic alterations are introduced, and in a percentage of cases epigenetic alterations remain after repair is completed, including increased methylation of CpG island promoters.^[45][46][47] Similar, but usually transient epigenetic alterations were recently found during repair of oxidative damages caused by H₂O₂, and it was suggested that occasionally these epigenetic alterations may also remain after repair.^[48] These accumulated epigenetic alterations may contribute to the epigenetic clock. Accumulation of epigenetic alterations may parallel the accumulation of un-repaired DNA damages that are proposed to cause aging (see DNA damage theory of aging).

Other age estimators 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 from blood that uses only three CpG sites of genes hardly affected by aging (cg25809905 in integrin, alpha 2b (ITGA2B); cg02228185 in aspartoacylase (ASPA) and cg17861230 in phosphodiesterase 4C, cAMP specific (PDE4C)).^[49] 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. ^[50] But the sparse estimator was developed for pyrosequencing data and is highly cost effective. ^[51]

2) Hannum et al. (2013) ^[5] report several age estimators: one for each tissue type. Each of these estimators requires covariate information (e.g. gender, body mass index, batch). The authors mention that each tissue led to 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 (due to poor calibration) as can be seen from Figure 4A in Hannum et al. (2013). Hannum et al. adjusted 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. cannot be used to compare the ages of different tissues/organs. In contrast, a salient characteristic of the epigenetic clock is that one does not have to carry out such a calibration step:^[1] it always uses the same CpGs and the same coefficient values. Therefore, Horvath's epigenetic clock can be used to compare the ages of different tissues/cells/organs from the same individual. While the age estimators from Hannum et al. cannot be used to compare the ages of different normal tissues, they can be used to compare the age of a cancerous tissue with that of a corresponding normal (non-cancerous) tissue. Hannum et al. reported pronounced age acceleration effects in all cancers. In contrast, Horvath's epigenetic clock ^[2][52] reveals that some cancer types (e.g. triple negative breast cancers or uterine corpus endometrial carcinoma) exhibit negative age acceleration, i.e. 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 new data involve different batches, one cannot 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 that tends to be strongly correlated with chronological age but may be poorly calibrated (i.e. 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.^[55] They wish to apply this method also to historical and relatively ancient human teeth.

In a multicenter benchmarking study 18 research groups from three continents compared all promising methods for analyzing DNA methylation in the clinic and identified the most accurate methods, having concluded that epigenetic tests based on DNA methylation are a mature technology ready for broad clinical use.^[56]

Other species

Wang et al., (in mice livers)^[57] and Petkovich et al.,(based on mice blood DNA methylation profiles)^[58] examined whether mice and humans experience similar patterns of change in the methylome with age. They found that mice treated with lifespan-extending interventions (surch as calorie restriction or dietary rapamycin) were significantly younger in epigenetic age than their untreated, wild-type age-matched controls. Mice age predictors also detects the longevity effects of gene knockouts, and rejuvenation of fibroblast-derived iPSCs.

Mice multi-tissue age predictor 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 sites in mouse for age predictions showed that human clock is not fully conserved in mouse.^[59] Differences between human and mouse clocks suggests that epigenetic clocks need to be trained specifically for different species.^[60]

Changes to DNA methylation patterns have great potential for age estimation and biomarker search in domestic and wild animals.^[61]