Insights into the origin of DNA methylation differences between monozygotic twins discordant for schizophrenia
Bibliographic record
Abstract
BACKGROUND: DNA methylation differences between monozygotic twins discordant for schizophrenia have been previously reported. However, the origin of methylation differences between monozygotic twins discordant for schizophrenia is not clear. The findings here argue that all DNA methylation differences may not necessarily represent the cause of the disease; rather some may result from the effect of antipsychotics. METHODS: Methylation differences in rat brain regions and also in two pairs of unrelated monozygotic twins discordant for schizophrenia have been studied using genome-wide DNA methylation arrays at Arraystar Inc. (Rockville, Maryland, USA). The identified gene promoters showing significant alterations to DNA methylation were then further characterized using ingenuity pathway analysis (Ingenuity System Inc, CA, USA). RESULTS: Pathway analysis of the most significant gene promoter hyper/hypomethylation revealed a significant enrichment of DNA methylation changes in biological networks and pathways directly relevant to neural development and psychiatric disorders. These included HIPPO signaling (p = 3.93E-03) and MAPK signaling (p = 4.27E-03) pathways involving hypermethylated genes in schizophrenia-affected patients as compared to their unaffected co-twins. Also, a number of significant pathways and networks involving genes with hypomethylated gene promoters have been identified. These included CREB signaling in neurons (p = 1.53E-02), Dopamine-DARPP32 feedback in cAMP signaling (p = 7.43E-03) and Ephrin receptors (p = 1.13E-02). Further, there was significant enrichment for pathways involved in nervous system development and function (p = 1.71E-03-4.28E-02). CONCLUSION: The findings highlight the significance of antipsychotic drugs on DNA methylation in schizophrenia patients. The unique pathways affected by DNA methylation in the two pairs of monozygotic twins suggest that patient-specific pathways are responsible for the disease; suggesting that patient-specific treatment strategies may be necessary in treating the disorder. The study reflects the need for developing personalized medicine approaches that take into consideration epigenetic variations between patients.
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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".