Customized MethylC-Capture Sequencing to Evaluate Variation in the Human Sperm DNA Methylome Representative of Altered Folate Metabolism
Bibliographic record
Abstract
Background: The sperm DNA methylation landscape is unique and critical for offspring health. If gamete-derived DNA methylation escapes reprograming in early embryos, epigenetic defects in sperm may be transmitted to the next generation. Current techniques to assess sperm DNA methylation show bias toward CpG-dense regions and do not target areas of dynamic methylation, those predicted to be environmentally sensitive and tunable regulatory elements. Objectives: Our goal was to assess variation in human sperm DNA methylation and design a targeted capture panel to interrogate the human sperm methylome. Methods: To characterize variation in sperm DNA methylation, we performed whole genome bisulfite sequencing (WGBS) on an equimolar pool of sperm DNA from a wide cross section of 30 men varying in age, fertility status, methylenetetrahydrofolate reductase (MTHFR) genotype, and exposures. With our targeted capture panel, in individual samples, we examined the effect of MTHFR genotype (n=13 677CC, n=8 677TT), as well as high-dose folic acid supplementation (n=6, per genotype, before and after supplementation). Results: Through WGBS we discovered nearly 1 million CpGs possessing intermediate methylation levels (20–80%), termed dynamic sperm CpGs. These dynamic CpGs, along with 2 million commonly assessed CpGs, were used to customize a capture panel for targeted interrogation of the human sperm methylome and test its ability to detect effects of altered folate metabolism. As compared with MTHFR 677CC men, those with the 677TT genotype (50% decreased MTHFR activity) had both hyper- and hypomethylation in their sperm. High-dose folic acid supplement treatment exacerbated hypomethylation in MTHFR 677TT men compared with 677CC. In both cases, >80% of altered methylation was found in dynamic sperm CpGs, uniquely measured by our assay. Discussion: Our sperm panel allowed the discovery of differential methylation following conditions affecting folate metabolism in novel dynamic sperm CpGs. Improved ability to examine variation in sperm DNA methylation can facilitate comprehensive studies of environment–epigenome interactions. https://doi.org/10.1289/EHP4812
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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".