# A Novel Framework for the Design of Minimized Epigenetic Clocks Using the Analysis of DNA Methylation Heterogeneity

**Authors:** Stanislav E. Romanov, Dmitry I. Karetnikov, Darya A. Kalashnikova, Denis E. Polivcev, Yakov A. Osipov, Daniil A. Maksimov, Polina A. Antoshina, Viktor V. Shloma, Ekaterina M. Samoilova, Alina A. Ivanova, Rustam F. Karimov, Artem N. Tkalin, Alexander A. Shevchenko, Vladimir A. Kalsin, Vladimir P. Baklaushev, Petr P. Laktionov

PMC · DOI: 10.3390/ijms26115051 · 2025-05-23

## TL;DR

This paper introduces a new framework for creating cost-effective, customized epigenetic clocks by analyzing DNA methylation heterogeneity in mesenchymal stem cells.

## Contribution

The study proposes a novel strategy for designing epigenetic clocks using targeted bisulfite sequencing and methylation heterogeneity.

## Key findings

- A minimized eAge model achieved good performance (MAE 1.094 and R2 0.897) in predicting cell passage.
- Combining average methylation levels with heterogeneity scores improves epigenetic clock quality.
- Targeted BS-seq enables analysis of longitudinal methylation changes for aging assessment.

## Abstract

Despite the significant progress made in the development of epigenetic age (eAge) clocks designed to estimate the various aspects of aging, currently available models, generated using large DNA methylation microarray datasets, still cannot fully address the issues of batch effects and technical variation. This hinders the use of the publicly available eAge clocks in routine laboratory practice, and it motivates the development of cost-effective, custom epigenetic clocks that are tailored to the given biological subjects and research methods. In this study, we analyzed the local DNA methylation of mesenchymal stem cell samples during culture expansion using high-throughput targeted bisulfite sequencing (BS-seq). Using the obtained data, we trained a minimized eAge model based on a Random Forest Regression with Leave-One-Out Cross-Validation, which determines cell passage with good performance (MAE 1.094 and R2 0.897) and which is comparable to previous solutions. Using the advantage of BS-seq to analyze consecutive CpGs methylation patterns, we demonstrated that combining the analysis of average DNA methylation levels with local methylation heterogeneity scores—thereby reflecting stochastic DNA methylation dynamics—can improve the quality of the epigenetic clock models. Therefore, we propose a research strategy for creating customized epigenetic clocks using targeted BS-seq and provide a mechanistic conceptualization of how information on longitudinal changes in DNA methylation patterns can potentially be used for the assessment of specific aging aspects.

## Full-text entities

- **Genes:** EDARADD (EDAR associated via death domain) [NCBI Gene 128178] {aka CR, ECTD11A, ECTD11B, ED3, EDA3}, PTPRC (protein tyrosine phosphatase receptor type C) [NCBI Gene 5788] {aka B220, CD45, CD45R, GP180, IMD105, L-CA}, CD44 (CD44 molecule (IN blood group)) [NCBI Gene 960] {aka CDW44, CSPG8, ECM-III, ECMR-III, H-CAM, HCELL}, NT5E (5'-nucleotidase ecto) [NCBI Gene 4907] {aka CALJA, CD73, E5NT, NT, NT5, NTE}, LTC4S (leukotriene C4 synthase) [NCBI Gene 4056], DPF3 (double PHD fingers 3) [NCBI Gene 8110] {aka BAF45C, CERD4, SMARCG3}, PDE4C (phosphodiesterase 4C) [NCBI Gene 5143] {aka DPDE1, PDE21}, ANKRD11 (ankyrin repeat domain 11) [NCBI Gene 29123] {aka ANCO-1, ANCO1, LZ16, T13}, CDKN2A (cyclin dependent kinase inhibitor 2A) [NCBI Gene 100271861] {aka INK4a, p14ARF, p16, p16INK4A}, FHL2 (four and a half LIM domains 2) [NCBI Gene 2274] {aka AAG11, DRAL, FHL-2, SLIM-3, SLIM3}, CDKN1A (cyclin dependent kinase inhibitor 1A) [NCBI Gene 474890] {aka p21}, DOK6 (docking protein 6) [NCBI Gene 220164] {aka DOK5L, HsT3226}, CD34 (CD34 molecule) [NCBI Gene 947], CDKN1A (cyclin dependent kinase inhibitor 1A) [NCBI Gene 1026] {aka CAP20, CDKN1, CIP1, MDA-6, P21, SDI1}, TNNI3 (troponin I3, cardiac type) [NCBI Gene 7137] {aka CMD1FF, CMD2A, CMH7, RCM1, TNNC1, cTnI}, THY1 (Thy-1 cell surface antigen) [NCBI Gene 7070] {aka CD90, CDw90}, PENK (proenkephalin) [NCBI Gene 5179] {aka PE, PENK-A}, HMGB2 (high mobility group box 2) [NCBI Gene 486068], CDKN2A (cyclin dependent kinase inhibitor 2A) [NCBI Gene 1029] {aka ARF, CAI2, CDK4I, CDKN2, CMM2, INK4}, ELOVL2 (ELOVL fatty acid elongase 2) [NCBI Gene 54898] {aka SSC2}, GLB1 (galactosidase beta 1) [NCBI Gene 2720] {aka EBP, ELNR1, MPS4B}, ADPGK (ADP dependent glucokinase) [NCBI Gene 83440] {aka 2610017G09Rik, ADP-GK}, LMNB1 (lamin B1) [NCBI Gene 4001] {aka ADLD, LMN, LMN2, LMNB, MCPH26}, ALOX12 (arachidonate 12-lipoxygenase, 12S type) [NCBI Gene 239] {aka 12-LOX, 12S-LOX, LOG12}, ITGB1 (integrin subunit beta 1) [NCBI Gene 3688] {aka CD29, FNRB, GPIIA, MDF2, MSK12, VLA-BETA}, MEG3 (maternally expressed 3) [NCBI Gene 55384] {aka FP504, GTL2, LINC00023, Lnc-DLK1-35, NCRNA00023, PRO0518}, PDK2 (pyruvate dehydrogenase kinase 2) [NCBI Gene 5164] {aka PDHK2, PDKII}, LMNB1 (lamin B1) [NCBI Gene 474663], ASPA (aspartoacylase) [NCBI Gene 443] {aka ACY2, ASP}, SEM1 (SEM1 26S proteasome subunit) [NCBI Gene 7979] {aka C7orf76, DSS1, ECD, PSMD15, SHFD1, SHFM1}, FPGT (fucose-1-phosphate guanylyltransferase) [NCBI Gene 8790] {aka GFPP}
- **Diseases:** PDR (MESH:D004410), inflammatory (MESH:D007249), injury to (MESH:D014947), MHL (MESH:C536761), cancer (MESH:D009369), precancerous (MESH:D011230)
- **Chemicals:** Ficoll (MESH:D005362), streptomycin (MESH:D013307), DPBS (MESH:C012939), FITC (MESH:D016650), SA (MESH:D000077145), penicillin (MESH:D010406), 5mC (-), nucleosides (MESH:D009705), CO2 (MESH:D002245), glucose (MESH:D005947)
- **Species:** Homo sapiens (human, species) [taxon 9606]
- **Mutations:** M300A

## Figures

5 figures with captions in the complete paper: https://tomesphere.com/paper/PMC12155483/full.md

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Source: https://tomesphere.com/paper/PMC12155483