Integrative DNA methylation and transcriptome analysis reveal cell-type specific patterns in response to elevated allostatic load
Bibliographic record
Abstract
Allostatic load (AL) is a measure of the body’s multi-systemic physiological dysregulation in response to chronic stress and life events. High AL has been associated with poor long-term health outcomes such as cardiovascular disease and mortality. DNA methylation (DNAm) is an epigenetic mechanism involving both genes and environmental factors and contributes to gene expression regulation. Hence, changes in AL can possibly be reflected in DNAm and gene expression differences and leveraging epigenetic and transcriptomic data together can help elucidate the underlying biological processes involved. To assess differential DNAm and gene expression between high and low AL in a cell-type specific manner, bulk DNAm and transcriptome signals from whole blood samples of 429 individuals from the Swiss Kidney Project On Genes in Hypertension (SKIPOGH) cohort were first deconvoluted into cell-type specific signals for six blood cell types using tensor composition analysis (TCA) and the software CIBERSORTx. For each cell type, DNAm associated with gene expression changes was then determined in high (N = 126) vs low (N = 303) AL groups. A total of 263 CpG-gene pairs were identified across all cell types, corresponding to 250 unique CpGs and 138 unique differentially methylated genes (DMGs). Several immune processes were enriched among downregulated genes of CD8 T and B cells, suggesting an impairment of the immune response, which is compatible with high AL. These findings highlight the importance of using cell-specific signals in DNAm and transcriptome analyses and may contribute to identify AL biomarkers and/or potential therapeutic targets.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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 teacher head, 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".