Variations in 5-methylcytosine and 5-hydroxymethylcytosine among human brain, blood, and saliva using oxBS and the Infinium MethylationEPIC array
Bibliographic record
Abstract
Investigating 5-methylcytosine (5mC) has led to many hypotheses regarding molecular mechanism underlying human diseases and disorders. Many of these studies, however, utilize bisulfite conversion alone, which cannot distinguish 5mC from its recently discovered oxidative product, 5-hydroxymethylcytosine (5hmC). Furthermore, previous array-based technologies do not have the necessary probes to adequately investigate both modifications simultaneously. In this manuscript, we used technical replicates of DNA from human brain, human blood, and human saliva, in combination with oxidative bisulfite conversion and Illumina's Infinium MethylationEPIC array, to analyze 5mC and 5hmC at more than 650 000 and 450 000 relevant loci, respectively, in the human genome. We show the presence of loci with detectable 5mC and 5hmC to be equally distributed across chromosomes and genomic features, while also being present in genomic regions with transcriptional regulatory properties. We also describe 2528 5hmC sites common across tissue types that show a strong association with immune-related functions. Lastly, in human brain, we show that 5hmC accounts for one-third of the total signal from bisulfite-converted data. As such, not only do our results confirm the efficacy and sensitivity of pairing oxidative bisulfite conversion and the EPIC array to detect 5mC and 5hmC in all three tissue types, but they also highlight the importance of dissociating 5hmC from 5mC in future studies related to cytosine modifications.
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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 | 0.000 |
| 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".