The role of epigenetic modifications in systemic autoinflammatory diseases
Bibliographic record
Abstract
Autoinflammatory diseases are a group of immune dysregulation disorders, with genetic mutations identified in approximately 50% of patients. However, patients with known pathogenic mutations may display a broad range of phenotypic diversity. Epigenetic modifications play a crucial role in regulating immune-mediated diseases, including autoinflammatory diseases, influencing disease course and complications. This review provides an overview of the current literature on the role of epigenetic changes in autoinflammatory diseases, examining their implications for disease mechanisms. To identify biological processes and pathways influenced by microRNAs across autoinflammatory diseases, we performed enrichment analyses in Gene Ontology biological processes. This analysis revealed enriched pathways reflecting the varying biological mechanisms implicated in familial Mediterranean fever (FMF), tumor necrosis factor associated periodic syndrome (TRAPS), and neonatal-onset multisystem inflammatory disease (NOMID). Notably, FMF was linked to clinically significant pathways, including those related to cytokine production, cardiovascular system, and neuroinflammation. These results highlight the potential of epigenetic modifications as biomarkers for autoinflammatory diseases. Given the role of epigenetic modifications in gene expression and the growing interest in their involvement in disease pathogenesis and therapy, including in rare disorders, further research into the epigenetic landscape of autoinflammatory diseases is essential. Furthermore, integrating genetic, epigenetic, and environmental factors will be key to addressing clinical variability and developing personalized treatment approaches.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".