The human placenta methylome
Bibliographic record
Abstract
Tissue-specific DNA methylation is found at promoters, enhancers, and CpG islands but also over larger genomic regions. In most human tissues, the vast majority of the genome is highly methylated (>70%). Recently, sequencing of bisulfite-treated DNA (MethylC-seq) has revealed large partially methylated domains (PMDs) in some human cell lines. PMDs cover up to 40% of the genome and are associated with gene repression and inactive chromatin marks. However, to date, only cultured cells and cancers have shown evidence for PMDs. Here, we performed MethylC-seq in full-term human placenta and demonstrate it is the first known normal tissue showing clear evidence of PMDs. We found that PMDs cover 37% of the placental genome, are stable throughout gestation and between individuals, and can be observed with lower sensitivity in Illumina 450K Infinium data. RNA-seq analysis confirmed that genes in PMDs are repressed in placenta. Using a hidden Markov model to map placental PMDs genome-wide and compare them to PMDs in other cell lines, we found that genes within placental PMDs have tissue-specific functions. For regulatory regions, methylation levels in promoter CpG islands are actually higher for genes within placental PMDs, despite the lower overall methylation of surrounding regions. Similar to PMDs, polycomb-regulated regions are hypomethylated but smaller and distinct from PMDs, with some being hypermethylated in placenta compared with other tissues. These results suggest that PMDs are a developmentally dynamic feature of the methylome that are relevant for understanding both normal development and cancer and may be of use as epigenetic biomarkers.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.012 | 0.005 |
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".