Biology subjects

Varshavsky, M.

Publications and source records attributed to Varshavsky, M..

3 recordsLinked to original sources

bioRxiv2025

An improved epigenetic age estimation with TFMethyl Clock reveals DNA methylation changes during aging in transcription factor binding sites

Methylation-based epigenetic clocks are among the most accurate tools for predicting chronological age. Although DNA methylation (DNAm) at genomic CpG sites is linked to various regulatory mechanisms, the biological interpretability of epigenetic clocks remains surprisingly limited. One primary mechanism by which DNAm is thought to influence gene regulation is by modulating transcription factor binding activity. In this study, we examine established epigenetic clocks to assess the regulatory potential of their predictive CpGs during the aging process. Our analysis reveals that generally most CpG sites used by epigenetic clocks do not overlap known transcription factor binding sites (TFBSs), indicating that changes in TFBS dynamics may not account for prediction accuracy of these models. On the other hand, by identifying age-associated CpGs that overlap TFBSs, we identified transcription factors that may be involved in the aging process. Specifically, the TFBSs of ZBED1, NFE2, CEBPB, FOXP1, EGR1, SP1, PAX5, and MAZ were particularly enriched for age-associated CpGs, while RBPJ, NFIC, RELA, IKZF1, STAT3, and USF2 were significantly protected against methylation changes. By focusing on TFBS-associated CpGs, combined with additional feature selection and engineering steps, we developed an alternative, TFMethyl Clock model, outperforming several existing approaches. Target genes of model-selected, age-predictive CpGs are enriched in the interleukin-1b production and long- chain fatty acid metabolism pathways. In contrast, these CpGs themselves are enriched mainly at binding sites of NR2C2 TF. Furthermore, approximately three-fourths of the target genes downstream of age- predictive CpGs exhibit significant age-related changes, suggesting that our approach captures deeper insights into possible methylation-driven biological aging processes. Our findings demonstrate that incorporating regulatory loci into the design of epigenetic predictors may provide mechanistic insights into the aging process while maintaining or even improving the predictive power.

bioinformatics↗

bioRxiv2024

Time is encoded by methylation changes at clustered CpG sites

Age-dependent changes in DNA methylation allow chronological and biological age inference, but the underlying mechanisms remain unclear. Using ultra-deep sequencing of >300 blood samples from healthy individuals, we show that age-dependent DNA methylation changes are regional and occur at multiple adjacent CpG sites, either stochastically or in a coordinated block-like manner. Deep learning analysis of single-molecule patterns in two genomic loci achieved accurate age prediction with a median error of 1.46-1.7 years on held-out human blood samples, dramatically improving current epigenetic clocks. Factors such as gender, BMI, smoking and other measures of biological aging do not affect chronological age inference. Longitudinal 10-year samples revealed that early deviations from epigenetic age are maintained throughout life and subsequent changes faithfully record time. Lastly, the model inferred chronological age from as few as 50 DNA molecules, suggesting that age is encoded by individual cells. Overall, DNA methylation changes in clustered CpG sites illuminate the principles of time measurement by cells and tissues, and facilitate medical and forensic applications. O_FIG O_LINKSMALLFIG WIDTH=174 HEIGHT=200 SRC="FIGDIR/small/626674v1_ufig1.gif" ALT="Figure 1"> View larger version (46K): org.highwire.dtl.DTLVardef@c4dd52org.highwire.dtl.DTLVardef@9e6e62org.highwire.dtl.DTLVardef@160f8c3org.highwire.dtl.DTLVardef@16be7a3_HPS_FORMAT_FIGEXP M_FIG C_FIG

genomics↗

bioRxiv2023

Accurate age prediction from blood using of small set of DNA methylation sites and a cohort-based machine learning algorithm

Chronological age prediction from DNA methylation sheds light on human aging, indicates poor health and predicts lifespan. Current clocks are mostly based on linear models from hundreds of methylation sites, and are not suitable for sequencing-based data. We present GP-age, an epigenetic clock for blood, that uses a non-linear cohort-based model of 11,910 blood methylomes. Using 30 CpG sites alone, GP-age outperforms state-of-the-art models, with a median accuracy of ~2 years on held-out blood samples, for both array and sequencing-based data. We show that aging-related changes occur at multiple neighboring CpGs, with far-reaching implications on aging research at the cellular level. By training three independent clocks, we show consistent deviations between predicted and actual age, suggesting individual rates of biological aging. Overall, we provide a compact yet accurate alternative to array-based clocks for blood, with future applications in longitudinal aging research, forensic profiling, and monitoring epigenetic processes in transplantation medicine and cancer. Graphical abstract O_FIG O_LINKSMALLFIG WIDTH=158 HEIGHT=200 SRC="FIGDIR/small/524874v1_ufig1.gif" ALT="Figure 1"> View larger version (31K): org.highwire.dtl.DTLVardef@14eb626org.highwire.dtl.DTLVardef@98aecdorg.highwire.dtl.DTLVardef@1fc2ca1org.highwire.dtl.DTLVardef@d6138f_HPS_FORMAT_FIGEXP M_FIG C_FIG O_LIMachine learning analysis of a large cohort (~12K) of DNA methylomes from blood C_LIO_LIA 30-CpG regression model achieves a 2.1-year median error in predicting age C_LIO_LIImproved accuracy ([≥]1.75 years) from sequencing data, using neighboring CpGs C_LIO_LIPaves the way for easy and accurate age prediction from blood, using NGS data C_LI MotivationEpigenetic clocks that predict age from DNA methylation are a valuable tool in the research of human aging, with additional applications in forensic profiling, disease monitoring, and lifespan prediction. Most existing epigenetic clocks are based on linear models and require hundreds of methylation sites. Here, we present a compact epigenetic clock for blood, which outperforms state-of-the-art models using only 30 CpG sites. Finally, we demonstrate the applicability of our clock to sequencing-based data, with far reaching implications for a better understanding of epigenetic aging.

bioinformatics↗