Analysis of epigenetic aging in vivo and in vitro: Factors controlling the speed and direction

TIME-seq reduces time and cost of DNA methylation measurement for epigenetic clock construction

Epigenetic 'clocks' based on DNA methylation have emerged as the most robust and widely used aging biomarkers, but conventional methods for applying them are expensive and laborious. Here we develop tagmentation-based indexing for methylation sequencing (TIME-seq), a highly multiplexed and scalable method for low-cost epigenetic clocks. Using TIME-seq, we applied multi-tissue and tissue-specific epigenetic clocks in over 1,800 mouse DNA samples from eight tissue and cell types. We show that TIME-seq clocks are accurate and robust, enriched for polycomb repressive complex 2-regulated loci, and benchmark favorably against conventional methods despite being up to 100-fold less expensive. Using dietary treatments and gene therapy, we find that TIME-seq clocks reflect diverse interventions in multiple tissues. Finally, we develop an economical human blood clock (R > 0.96, median error = 3.39 years) in 1,056 demographically representative individuals. These methods will enable more efficient epigenetic clock measurement in larger-scale human and animal studies.

DNA Methylation Changes in Autologous Hematopoietic Stem Cell Transplant Patients

BACKGROUND

Blood cancers may be potentially cured with hematopoietic stem cell transplantation (HCT); however, standard pre-assessments for transplant eligibility do not capture all contributing factors for transplant outcomes. Epigenetic biomarkers predict outcomes in various diseases. This pilot study aims to explore epigenetic changes (epigenetic age and differentially methylated genes) in patients before and after autologous HCT, that can serve as potential biomarkers to better predict HCT outcomes.

METHODS

This study used a prospective longitudinal study design to compare genome wide DNA methylation changes in 36 autologous HCT eligible patients recruited from the Cellular Immunotherapies and Transplant clinic at a designated National Cancer Center.

RESULTS

Genome-wide DNA methylation, measured by the Illumina Infinium Human Methylation 850K BeadChip, showed a significant difference in DNA methylation patterns post-HCT compared to pre-HCT. Compared to baseline levels of DNA methylation pre-HCT, 3358 CpG sites were hypo-methylated and 3687 were hyper-methylated. Identified differentially methylated positions overlapped with genes involved in hematopoiesis, blood cancers, inflammation and immune responses. Enrichment analyses showed significant alterations in biological processes such as immune response and cell structure organization, however no significant pathways were noted. Though participants had an advanced epigenetic age compared to chronologic age before and after HCT, both epigenetic age and accelerated age decreased post-HCT.

CONCLUSION

Epigenetic changes, both in epigenetic age and differentially methylated genes were observed in autologous HCT recipients, and should be explored as biomarkers to predict transplant outcomes after autologous HCT in larger, longitudinal studies.

Associations of Plasma Folate and Vitamin B6 With Blood DNA Methylation Age: An Analysis of One-Carbon Metabolites in the VA Normative Aging Study

One-carbon metabolism is an important contributor to aging-related diseases; nevertheless, relationships of one-carbon metabolites with novel DNA methylation-based measures of biological aging remain poorly characterized. We examined relationships of one-carbon metabolites with 3 DNA methylation-based measures of biological aging: DNAmAge, GrimAge, and PhenoAge. We measured plasma levels of 4 common one-carbon metabolites (vitamin B6, vitamin B12, folate, and homocysteine) in 715 VA Normative Aging Study participants with at least 1 visit between 1999 and 2008 (observations = 1153). DNA methylation age metrics were calculated using the HumanMethylation450 BeadChip. We utilized Bayesian Kernel Machine Regression models adjusted for chronological age, lifestyle factors, age-related diseases, and study visits to determine metabolites important to the aging outcomes. Bayesian Kernel Machine Regression models allowed for the estimation of the relationships of single metabolites and the cumulative metabolite mixture with methylation age. Log vitamin B6 was selected as important to PhenoAge (β = -1.62 years, 95% CI: -2.28, -0.96). Log folate was selected as important to GrimAge (β = 0.75 years, 95% CI: 0.41, 1.09) and PhenoAge (β = 1.62 years, 95% CI: 0.95, 2.29). Compared to a model where each metabolite in the mixture is set to its 50th percentile, the log cumulative mixture with each metabolite at its 30th (β = -0.13 years, 95% CI: -0.26, -0.005) and 40th percentile (β = -0.06 years, 95% CI: -0.11, -0.005) was associated with decreased GrimAge. Our results provide novel characterizations of the relationships between one-carbon metabolites and DNA methylation age in a human population study. Further research is required to confirm these findings and establish their generalizability.