Gene-body 5-hydroxymethylation is associated with gene expression changes in the prefrontal cortex of depressed individuals
Bibliographic record
Abstract
5-Hydroxymethylcytosine (5hmC) is a recently characterized epigenetic mark that is particularly abundant in brain tissue and that regulates gene transcription. We have recently begun to understand the important role of 5hmC in brain development, plasticity and disease, but there are currently little data on 5hmC alterations in psychiatric illnesses. Here we report what we believe to be the first genome-wide analysis of 5hmC in the depressed brain. Using AbaSI sequencing, we investigated 5hmC in the prefrontal cortex of depressed (N=19) and psychiatrically healthy controls (N=19). Consistent with previous global 5hmC analyses in other phenotypes, and likely owing to the inter-individual variability in 5hmC content, the distribution of 5hmC across chromosomes and genomic features was not different between groups. We did, however, find 550 CpGs with suggestive evidence of differential hydroxymethylation. Of these, we validated CpGs in the gene body of myosin XVI (MYO16) and insulin-degrading enzyme using targeted oxidative bisulfite sequencing. Furthermore, the enrichment of 5hmC was also associated with changes in the expression of these two genes in depressed suicides. Together, our results present a novel mechanism linking increased 5hmC to depression and provide a framework for future research in this field.
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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 | 0.000 | 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) | 0.001 | 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".