Whole genome bisulfite sequencing of Down syndrome brain reveals regional DNA hypermethylation and novel disorder insights
Bibliographic record
Abstract
Down Syndrome (DS) is the most common genetic cause of intellectual disability, in which an extra copy of human chromosome 21 (HSA21) affects regional DNA methylation profiles across the genome. Although DNA methylation has been previously examined at select regulatory regions across the genome in a variety of DS tissues and cells, differentially methylated regions (DMRs) have yet to be examined in an unbiased sequencing-based approach. Here, we present the first analysis of DMRs from whole genome bisulfite sequencing (WGBS) data of human DS and matched control brain, specifically frontal cortex. While no global differences in DNA methylation were observed, we identified 3,152 DS-DMRs across the entire genome, the majority of which were hypermethylated in DS. DS-DMRs were significantly enriched at CpG islands and de-enriched at specific gene body and regulatory regions. Functionally, the hypermethylated DS-DMRs were enriched for one-carbon metabolism, membrane transport, and glutamatergic synaptic signalling, while the hypomethylated DMRs were enriched for proline isomerization, glial immune response, and apoptosis. Furthermore, in a cross-tissue comparison to previous studies of DNA methylation from diverse DS tissues and reference epigenomes, hypermethylated DS-DMRs showed a strong cross-tissue concordance, while a more tissue-specific pattern was observed for the hypomethylated DS-DMRs. Overall, this approach highlights that low-coverage WGBS of clinical samples can identify epigenetic alterations to known biological pathways, which are potentially relevant to therapeutic treatments and include metabolic pathways. These results also provide new insights into the genome-wide effects of genetic alterations on DNA methylation profiles indicative of altered neurodevelopment and brain function.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 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) | 0.000 | 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".