Characterizing 5-hydroxymethylcytosine in human prefrontal cortex at single base resolution
Bibliographic record
Abstract
BACKGROUND: The recent discovery that methylated cytosines are converted to 5-hydroxymethylated cytosines (5hmC) by the family of ten-eleven translocation enzymes has sparked significant interest on the genomic location, the abundance in different tissues, the putative functions, and the stability of this epigenetic mark. 5hmC plays a key role in the brain, where it is particularly abundant and dynamic during development. RESULTS: Here, we comprehensively characterize 5hmC in the prefrontal cortices of 24 subjects. We show that, although there is inter-individual variability in 5hmC content among unrelated individuals, approximately 8 % of all CpGs on autosomal chromosomes contain 5hmC, while sex chromosomes contain far less. Our data also provide evidence suggesting that 5hmC has transcriptional regulatory properties, as the density of 5hmC was highest in enhancer regions and within exons. Furthermore, we link increased 5hmC density to histone modification binding sites, to the gene bodies of actively transcribed genes, and to exon-intron boundaries. Finally, we provide several genomic regions of interest that contain gender-specific 5hmC. CONCLUSIONS: Collectively, these results present an important reference for the growing number of studies that are interested in the investigation of the role of 5hmC in brain and mental disorders.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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 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".