DNA Methylation: A Biomarker of the Epigenetic Clock in Aging

A special issue of Methods and Protocols (ISSN 2409-9279).

Deadline for manuscript submissions: closed (30 August 2020) | Viewed by 2724

Special Issue Editor


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Guest Editor
1. Beth Israel Deaconess Medical Center, Department of Medicine, Harvard Medical School, Boston, MA 02215, USA
2. Harvard Medical School Initiative for RNA Medicine, Harvard Medical School, Boston, MA 02115, USA
3. Beth Israel Deaconess Medical Center, Cancer Research Institute, Boston, MA 02215, USA
Interests: DNA methylation; epigenetics; noncoding RNAs; RNA therapeutics

Special Issue Information

Dear Colleagues,

One of the most fascinating biological questions is: “why do we age?”

Chronological age is not a reliable measure to assess the aging process. If we want to understand and control aging, we need instead accurate markers to evaluate biological age. 

Various studies in humans and murine models have shown that aging is associated with DNA methylation changes. DNA methylation is a key epigenetic signature implicated in regulation of gene expression and occurs predominantly within CpG dinucleotides. Methylation of CpG-rich promoters is carried out by DNA methyltransferases (DNMTs). Although CpG dinucleotides are underrepresented in the mammalian genome, they tend to cluster within CpG-rich regions—CpG islands (CGI), located in the proximity of the transcription start sites of most protein-coding genes. Further, while 70% to 80% of the CpGs in the entire genome are methylated, CGIs remain mostly unmethylated in somatic cells.

The identification of the “epigenetic clocks”, a set of CpG sites whose DNA methylation levels can be used to measure subject age, has unveiled novel molecular targets to monitor the aging process and to define the “epigenetic age”.

Despite outstanding progress, many challenging questions still remain unanswered: “Can we separate drivers versus passengers of age-associated changes in single-cell, tissue-specific physiological or pathological conditions? Do the DNA methylation-based epigenetic clocks correlate with other epigenetic marks? How do “epigenetic age and rate” change in disease state? Is DNA methylation an accurate biomarker of healthy versus unhealthy aging? To put it differently, is DNA methylation an inner biological clock?”

A multidisciplinary and comprehensive approach integrating various scientific disciplines such as biology, computational and evolutionary biology, bioinformatics, and molecular medicine will be required to delve into these questions and understand how the molecular gears of the epigenetic clocks function.

By answering these questions, the Special Issue aims at elucidating the role of DNA methylation as a “biomarker” of the epigenetic clock in aging. We welcome the submission of both original research and review articles introducing new molecular, bioinformatics, and computational biology strategies to identify DNA methylation-based indicators of the epigenetic clock, which will enable the discovery of potential targets for clinical intervention in aging.

Prof. Dr. Annalisa Di Ruscio, MD, PHD
Guest Editor

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Keywords

  • aging
  • DNA methylation
  • biomarkers
  • epigenetic clocks
  • epigenetic changes
  • epigenetic age

Published Papers (1 paper)

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9 pages, 732 KiB  
Technical Note
Prediction of Lung Function in Adolescence Using Epigenetic Aging: A Machine Learning Approach
by Md Adnan Arefeen, Sumaiya Tabassum Nimi, M. Sohel Rahman, S. Hasan Arshad, John W. Holloway and Faisal I. Rezwan
Methods Protoc. 2020, 3(4), 77; https://doi.org/10.3390/mps3040077 - 09 Nov 2020
Cited by 5 | Viewed by 2323
Abstract
Epigenetic aging has been found to be associated with a number of phenotypes and diseases. A few studies have investigated its effect on lung function in relatively older people. However, this effect has not been explored in the younger population. This study examines [...] Read more.
Epigenetic aging has been found to be associated with a number of phenotypes and diseases. A few studies have investigated its effect on lung function in relatively older people. However, this effect has not been explored in the younger population. This study examines whether lung function in adolescence can be predicted with epigenetic age accelerations (AAs) using machine learning techniques. DNA methylation based AAs were estimated in 326 matched samples at two time points (at 10 years and 18 years) from the Isle of Wight Birth Cohort. Five machine learning regression models (linear, lasso, ridge, elastic net, and Bayesian ridge) were used to predict FEV1 (forced expiratory volume in one second) and FVC (forced vital capacity) at 18 years from feature selected predictor variables (based on mutual information) and AA changes between the two time points. The best models were ridge regression (R2 = 75.21% ± 7.42%; RMSE = 0.3768 ± 0.0653) and elastic net regression (R2 = 75.38% ± 6.98%; RMSE = 0.445 ± 0.069) for FEV1 and FVC, respectively. This study suggests that the application of machine learning in conjunction with tracking changes in AA over the life span can be beneficial to assess the lung health in adolescence. Full article
(This article belongs to the Special Issue DNA Methylation: A Biomarker of the Epigenetic Clock in Aging)
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