Time is encoded by methylation changes at clustered CpG sites.

Area of Science:

• Epigenetics
• Genomics
• Computational Biology

Background:

• Age-dependent DNA methylation changes are key to inferring chronological and biological age.
• The precise mechanisms driving these age-related methylation shifts remain largely unknown.

Purpose of the Study:

• To investigate the mechanisms of age-dependent DNA methylation changes.
• To develop highly accurate methods for chronological age prediction using DNA methylation patterns.
• To explore the cellular basis of age encoding in DNA.

Main Methods:

• Ultra-deep sequencing of >300 blood samples from healthy individuals.
• Analysis of regional CpG site methylation patterns (stochastic vs. block-like).
• Application of deep learning models to single-molecule methylation data from specific genomic loci.

Main Results:

• Age-dependent methylation changes occur regionally in clustered CpG sites, either stochastically or in blocks.
• Deep learning models accurately predicted chronological age (1.36-1.7 years median error) using single-molecule patterns.
• Predictions were robust across various factors like sex, smoking, and BMI.
• Longitudinal analysis confirmed the persistence of age deviations and the faithful recording of time by methylation changes.
• Accurate age prediction was achieved with as few as 50 DNA molecules, indicating cellular-level age encoding.

Conclusions:

• Clustered DNA methylation changes provide insights into cellular and tissue time measurement.
• The findings significantly improve chronological age prediction accuracy.
• The study suggests that individual cells encode chronological age.
• The developed methods have potential applications in medicine and forensics.