Report on DNA Methylation Clocks: Alternate Designs and Applications

This report examines DNA methylation clocks, focusing on the Horvath clock model and the role of DNA methylation in aging. It details the biological underpinnings, including the addition of methyl groups to cytosine, and the development of CpG nucleotides. The report also explores alternate clock designs, such as the 391 CpGs clock, and the Hannum clock, highlighting their applications in estimating DNA methylation age across various tissues and their potential in forensic procedures and mortality analysis. Furthermore, it discusses the impact of DNA methylation changes and cell-cell heterogeneity, using an experiment on yeast communities to illustrate how metabolic cooperation and stress factors influence cellular behavior and heterogeneity, ultimately connecting these findings to the causes and consequences of methylation clocks and age-related/disease-related outcomes. The report concludes by referencing key studies and research in the field.

DNA methylation clock (using Horvath clock model)
DNA Horvath epigenetic clock is based on the addition of the methyl group across the C-5
cytosine location to facilitate the generation of 5mC (5-methyl cytosine) (1). The guanine
residue becomes the secondary methylation target. DNA methylation pathways (i.e. clocks)
lead to the development of CpG nucleotide. DNA methyltransferase-1, 3A, and 3B enzymes
facilitate the transfer of S-adenosylmethionine-based methyl group towards the DNA. DNA
Methylation Epigenetic Clock (i.e. 353CpGs) is segregated into two different types based on
the age correlation (2).
Biological Underpinnings
Hyperventilation and hypermethylation of the 160 and 193 negatively and positively
correlated CpGs occurs along with the age advancement in the individuals (2). The variation
of the 193 positively correlated CpGs occurs less frequently in comparison to the 160
negatively correlated CpGs. DNA methylation pattern effectively stabilizes the chromatin
composition and structure. The methyl group positioning across the CG dinucleotide is
controlled by the significant epigenetic markers. The environmental exposure impacts the
age-based biomarkers and related DNA-methylation alterations that results in the
development of various diseases (2).
Alternate clock design
Biomarkers of DNA methylation exhibit sub-optimal performance across the fibroblasts in
various ex-vivo interventions (3). Therefore, development of the 391 CpGs alternate clock
design assists in evaluating the age of DNA methylation across the human fibroblasts (3).
Hannum’s clock is one other methylation clock that impacts the blood methylation level to a
considerable extent (4).
Why the chosen alternate design is interesting?
This alternate 391 CpGs clock design helps in estimating the DNA methylation age for
saliva, blood, lymphoblastoid cells, endothelial cells, buccal cells, and keratinocytes (3). 391
CpGs clock design sensitively tracks the aging of bone, liver, brain, glia, and neurons. This
methylation clock is expected to significantly assist the development of human cell aging
assay and forensic procedures (3). Furthermore, the Hannum’s clock assists in finding the
mortality prevalence and its causes in the context of evaluating the molecular age of
individuals (5).

The cause of DNA methylation changes based on DNA methylation clocks knowledge
DNA methylation pattern is substantially governed by DNA methylation clocks that
significantly alter their mechanism over time (6). The age-based modification of the
epigenetic markers longitudinally alters the DNA methylation pattern among people. The age
advancement, behavioural/lifestyle changes, and familial clustering elevate the pace of
histone alterations that resultantly impacts the structure and function of nucleosomes along
with the chromatin folding order. The genetic modifications based on DNA methylation
significantly change the expression of the immune system regulating genes. Eventually, the
human body experiences elevated predisposition for epigenetic defects and chronic diseases
with age advancement (6).
Cell-cell heterogeneity (Diagram’s question discussion)
The differences in cellular behavior, structure, and function are based on the cell-cell
heterogeneity and distribution pattern (7). The genetically similar cells continue to exhibit
cell-cell heterogeneity and respond variably to stressful situations (7).
Experiment to address the diagram's question
The experiment by Campbell, Vowinckel, and Ralser evaluated the pattern of stress-based
heterogeneity across the self-establishing yeast communities (8). Experimental outcomes
revealed metabolic specialization (of the yeast cells) as the significant cause of the stress
heterogeneity pattern across several yeast communities. The heterogeneity pattern in the
metabolically cooperating yeast communities emerged under the impact of heat stress and
diamide treatments that resulted in variable metabolic exchange interactions between the
amino acids including methionine, uracil, leucine, and histidine. The metabolically
cooperating yeast community exhibited collective tolerance against the induced stress level.
However, the individual yeast cells showed different responsiveness and phenotypic
heterogeneity pattern despite tolerating the stress level under metabolic cooperation. This
phenotypic heterogeneity resulted under the impact of the metabolic exchange interaction and
the auxotrophic marker genes of the SeMeCo (self‐establishing and metabolically
cooperating) yeast communities. Indeed, the cell-cell heterogeneity is directly related to the
significant consequences and causes of the methylation clocks (9). Therefore, an experiment
to check the cellular heterogeneity must evaluate the stress factors and their impact on
respective biomarkers in the context of understanding the age-related/disease-related
outcomes/changes (9).

References
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1. Moskalev A, Vaiserman AM. Epigenetics of Aging and Longevity: Translational
Epigenetics London: Academic Press Elsevier; 2018.
2. Horvath S. DNA methylation age of human tissues and cell types. Genome Biol. 2013;
14(10).
3. Horvath S, Oshima J, Martin GM, Lu AK, Quach A, Cohen H, et al. Epigenetic clock for
skin and blood cells applied to Hutchinson Gilford Progeria Syndrome and ex vivo
studies. Aging (Albany NY). 2018; 10(7): p. 1758–1775.
4. Bjornsson HT, Sigurdsson MI, Fallin MD, Irizarry RA, Aspelund T, Cui H, et al. Intra-
individual change in DNA methylation over time with familial clustering. JAMA. 2008;
299(24): p. 2877–2883.
5. Altschuler SJ, Wu LF. Cellular heterogeneity: when do differences make a difference?
Cell. 2010; 141(4): p. 559–563.
6. Campbell K, Vowinckel J, Ralser M. Cell‐to‐cell heterogeneity emerges as consequence
of metabolic cooperation in a synthetic yeast community. Biotechnol J. 2016; 11(9): p.
1169–1178.
7. Field AE, Robertson NA, Wang T, Havas A, Ideker T, Adams PD. DNA Methylation
Clocks in Aging: Categories, Causes, and Consequences. Molecular Cell. 2018
September;: p. 882-895.
8. Horvath S, Gurven M, Levine ME, Trumble BC, Kaplan H, Allayee H, et al. An
epigenetic clock analysis of race/ethnicity, sex, and coronary heart disease. Genome Biol.
2016;: p. 1-22.
9. Jung SE, Shin KJ, Lee HY. DNA methylation-based age prediction from various tissues
and body fluids. BMB Rep. 2017; 50(11): p. 546–553.
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