<HashMap><database>biostudies-arrayexpress</database><scores/><additional><omics_type>Unknown</omics_type><omics_type>Transcriptomics</omics_type><omics_type>Genomics</omics_type><omics_type>Proteomics</omics_type><submitter>Dayeon Kang</submitter><instrument_platform>Illumina NovaSeq 6000</instrument_platform><instrument_platform>-</instrument_platform><study_type>RNA-seq of coding RNA</study_type><organism>Canis lupus familiaris</organism><species>Canis lupus familiaris</species><full_dataset_link>https://www.ebi.ac.uk/biostudies/studies/E-MTAB-15574</full_dataset_link><description>Epigenetic drift, the stochastic accumulation of DNA methylation changes over time, contributes to transcriptional variability in aging. To investigate this, whole blood samples from 24 beagles in three age groups (3, 5, and 10 years) were analyzed using whole-genome bisulfite sequencing (WGBS) and RNA sequencing (RNA-seq). DNA methylation was profiled at approximately 20 million CpG sites, and gene expression was quantified across 30 thousand genes to assess age-related epigenetic and transcriptional changes. Actively expressed genes exhibited lower methylation levels near transcription start sites compared to non-expressed genes across all dogs, highlighting the role of DNA methylation in gene regulation. We then observed an increased degree of DNA methylation drift and expression level variability in aged dogs, potentially indicating a disruption in stable regulatory patterns and molecular heterogeneity. Pairwise comparisons across age groups identified 2,320 age-associated differentially methylated regions (DMRs) encompassing 39,369 CpGs, which were significantly enriched in exon and promoter regions, suggesting their non-random distribution in the genome. Additionally, we identified age-associated DNA methylation and gene expression changes using unsupervised cluster analysis classified into three patterns: early-to-mid, mid-to-late, and progressive. Gene sets across all patterns were commonly overrepresented in the immune system, morphogenesis, and signal transduction pathways. Notably, mid-to-late transition clusters showed a strong association with cell cycle regulation and cellular senescence, suggesting that epigenetic modifications may contribute to dysregulated cell cycle control and reduced regenerative capacity. This study provides insights into nonlinear and linear aging trajectories by integrating epigenomic and transcriptomic features, revealing regulatory mechanisms of age-related molecular changes.</description><repository>biostudies-arrayexpress</repository><sample_protocol>Library Construction - Libraries were prepared for 150 bp paired-end sequencing using a TruSeq Stranded mRNA Library Prep Kit (Illumina, San Diego, CA, USA).</sample_protocol><sample_protocol>Sample Collection - A total of 24 beagle dogs aged 3 years (n = 8), 5 years (n = 8), and 10 years (n = 8) were investigated for age-related molecular signatures in their methylomes. All dogs were kept at the NIAS under standardized housing conditions that were maintained through continuous monitoring, vaccination, and anthelmintic treatment. The experiment was ethically approved by the Institutional Animal Care and Use Committee of the National Institute of Animal Science (NIAS 2022-586) and Gyeongsang National University (GNU-240802-D0152-01).</sample_protocol><sample_protocol>Nucleic Acid Extraction - Whole blood samples were collected using a 21-gauge needle and 5-mL syringe from the jugular vein of each dog. RNA from whole blood was extracted using a PAXgene Blood RNA Kit (762174; QIAGEN, Germantown, MD, USA) following the manufacturer’s instructions.</sample_protocol><sample_protocol>Sequencing - The paired-end sequencing (2×150 bp) was performed using Illumina NovaSeq 6000 (Illumina, San Diego, CA, USA).</sample_protocol><figure_sub>Organization</figure_sub><figure_sub>MINSEQE Score</figure_sub><figure_sub>Assays and Data</figure_sub><figure_sub>MAGE-TAB Files</figure_sub><pubmed_authors>Dayeon Kang</pubmed_authors></additional><is_claimable>false</is_claimable><name>Genome-wide methylation drift and transcriptomic variability in aged beagle dogs - RNA-seq</name><description>Epigenetic drift, the stochastic accumulation of DNA methylation changes over time, contributes to transcriptional variability in aging. To investigate this, whole blood samples from 24 beagles in three age groups (3, 5, and 10 years) were analyzed using whole-genome bisulfite sequencing (WGBS) and RNA sequencing (RNA-seq). DNA methylation was profiled at approximately 20 million CpG sites, and gene expression was quantified across 30 thousand genes to assess age-related epigenetic and transcriptional changes. Actively expressed genes exhibited lower methylation levels near transcription start sites compared to non-expressed genes across all dogs, highlighting the role of DNA methylation in gene regulation. We then observed an increased degree of DNA methylation drift and expression level variability in aged dogs, potentially indicating a disruption in stable regulatory patterns and molecular heterogeneity. Pairwise comparisons across age groups identified 2,320 age-associated differentially methylated regions (DMRs) encompassing 39,369 CpGs, which were significantly enriched in exon and promoter regions, suggesting their non-random distribution in the genome. Additionally, we identified age-associated DNA methylation and gene expression changes using unsupervised cluster analysis classified into three patterns: early-to-mid, mid-to-late, and progressive. Gene sets across all patterns were commonly overrepresented in the immune system, morphogenesis, and signal transduction pathways. Notably, mid-to-late transition clusters showed a strong association with cell cycle regulation and cellular senescence, suggesting that epigenetic modifications may contribute to dysregulated cell cycle control and reduced regenerative capacity. This study provides insights into nonlinear and linear aging trajectories by integrating epigenomic and transcriptomic features, revealing regulatory mechanisms of age-related molecular changes.</description><dates><release>2026-01-01T00:00:00Z</release><modification>2026-01-02T02:03:08.754Z</modification><creation>2025-09-12T11:35:49.619Z</creation></dates><accession>E-MTAB-15574</accession><cross_references><ENA>ERP180010</ENA><EFO>EFO_0002944</EFO><EFO>EFO_0004170</EFO><EFO>EFO_0005518</EFO><EFO>EFO_0003738</EFO><EFO>EFO_0004184</EFO></cross_references></HashMap>