Building a bovine blood DNA methylation epimap related to disease phenotypes
Bibliographic record
Abstract
BACKGROUND: Epigenetic variations, particularly in response to environmental factors, play a crucial role in shaping immune identity and function in hematopoietic cells. This study investigates interindividual differences in DNA methylation among dairy cows, with the aim of enhancing our understanding of the adaptive capacity essential for sustainable animal production. We conducted whole-genome sequencing and DNA methylation analysis using enzymatic methyl-seq on whole blood from 60 Holstein cows. The study included five phenotype groups: mastitis, lameness, infertility, metabolic disorders and healthy controls. RESULTS: Among the 50 million CpG sites, 5.1% were identified as variable methylated cytosines (VMCs) and 94.9% as conserved methylated cytosines (CMCs). VMCs displayed variability in distal promoter regions, suggesting potential plasticity in the associated genes, while CMCs exhibited a bimodal methylation pattern near the transcription start site, indicative of tissue-specific functions. Notably, we identified motif enrichments related to genes potentially expressed in blood. An age-related analysis revealed a 1.4% faster decline in CMCs methylation compared to VMCs. Additionally, disease risk assessment may be achievable using as few as 586 methylation biomarkers, which could be used to select which cows to keep in the herd for additional lactation. CONCLUSION: Our results suggest a dual role for VMCs and CMCs: while the stability of conserved sites is potentially associated with essential functions in cell development and homeostasis, variable sites may be involved in dynamically regulating gene transcription in response to internal or external stimuli. These insights underscore the epigenome's role in immune regulation and adaptive resilience in cattle.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".