Tissue‐specific dysregulation of DNA methylation in aging
Bibliographic record
Abstract
The normal aging process is a complex phenomenon associated with physiological alterations in the function of cells and organs over time. Although an attractive candidate for mediating transcriptional dysregulation, the contribution of epigenetic dysregulation to these progressive changes in cellular physiology remains unclear. In this study, we employed the genome-wide HpaII tiny fragment enrichment by ligation-mediated PCR assay to define patterns of cytosine methylation throughout the rat genome and the luminometric methylation analysis assay to measure global levels of DNA methylation in the same samples. We studied both liver and visceral adipose tissues and demonstrated significant differences in DNA methylation with age at > 5% of sites analyzed. Furthermore, we showed that epigenetic dysregulation with age is a highly tissue-dependent phenomenon. The most distinctive loci were located at intergenic sequences and conserved noncoding elements, and not at promoters nor at CG-dinucleotide-dense loci. Despite this, we found that there was a subset of genes at which cytosine methylation and gene expression changes were concordant. Finally, we demonstrated that changes in methylation occur consistently near genes that are involved in metabolism and metabolic regulation, implicating their potential role in the pathogenesis of age-related diseases. We conclude that different patterns of epigenetic dysregulation occur in each tissue over time and may cause some of the physiological changes associated with normal aging.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".