Epigenetic insights into fertility: involvement of immune cell methylation in dairy cows reproduction
Bibliographic record
Abstract
Infertility and post-partum reproductive diseases are significant challenges in cattle farming, with the maternal immune system's ability to recognize and tolerate the embryo being crucial for successful gestation. DNA methylation in hematopoietic cells may influence susceptibility to post-partum fertility issues, making the identification of epigenetic changes vital for sustainable animal production. This study aimed to characterize the methylome of immune cells in relation to fertility, potentially enabling early detection of subfertility. Using whole epigenome sequencing and enzymatic methyl-seq, we analyzed DNA methylation patterns in blood from twelve Holstein cows before the onset of any disease. Our findings revealed 216,990 differentially methylated cytosines (DMCs) between fertile and subfertile cows. Notably, three genes-Interferon tau-3 (IFNT3), KIAA0825, and RAS-Related Protein 2A-showed high significance in their differential methylation between fertile and subfertile cows. IFNT3, crucial for early embryonic development, had seven DMCs in its TSS shores in subfertile cows. Additionally, the KLRA1 gene (Ly49), was identified as containing DMCs across all five genomic regions analyzed (TSS shores, exons, introns, downstream, and distal intergenic). Its widespread differential methylation highlights its potential impact on fertility. Key interleukin genes, including IL6, IL15, IL22, and IL36G, also showed multiple DMCs, reinforcing the role of the immune system in bovine fertility. These findings illustrate the potential control that immune cell epigenetics exert on cattle post-partum fertility. Additionally, this study suggests that the risk of developing subfertility could potentially be estimated with as few as 220 biomarkers, paving the way for enhanced animal health management and improved fertility treatments.
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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 | 0.000 |
| 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 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".