Whole genome methylation analyses of schizophrenia patients before and after treatment
Bibliographic record
Abstract
The aetiology of schizophrenia is still unknown but it involves both heritable and non-heritable factors. DNA methylation is an inheritable epigenetic modification that stably alters gene expression. It takes part in the regulation of neurodevelopment and may be a contributing factor to the pathogenesis of brain diseases. It was found that many of the antipsychotic drugs may lead to epigenetic modifications. We have performed 42 high-resolution genome-wide methylation array analyses to determine the methylation status of 27,627 CpG islands. Differentially methylated regions were studied with samples from 20 Bulgarian individuals divided in four groups according to their gender (12 males/8 females) and their treatment response (6 in complete/14 in incomplete remission). They were compared to two age and sex matched control pools (110 females in female pool/110 males in male pool) before and after treatment. We found significant differences in the methylation profiles between male schizophrenia patients with complete remission and control male pool before treatment (C16orf70, CST3, DDRGK1, FA2H, FLJ30058, MFSD2B, RFX4, UBE2J1, ZNF311) and male schizophrenia patients with complete remission and control male pool after treatment (AP1S3, C16orf59, KCNK15, LOC146336, MGC16384, XRN2) that potentially could be used as target genes for new therapeutic strategies as well as markers for good treatment response. Our data revealed major differences in methylation profiles between male schizophrenia patients in complete remission before and after treatment and healthy controls which supports the hypothesis that antipsychotic drugs may play a role in epigenetic 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 | 0.000 |
| 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".