Neuroinflammation and histone H3 citrullination are increased in X-linked Dystonia Parkinsonism post-mortem prefrontal cortex
Bibliographic record
Abstract
Neuroinflammation plays a pathogenic role in neurodegenerative diseases and recent findings suggest that it may also be involved in X-linked Dystonia-Parkinsonism (XDP) pathogenesis. Previously, fibroblasts and neuronal stem cells derived from XDP patients demonstrated hypersensitivity to TNF-α, dysregulation in NFκB signaling, and an increase in several pro-inflammatory markers. However, the role of inflammatory processes in XDP patient brain remains unknown. Here we demonstrate that there is a significant increase in astrogliosis and microgliosis in human post-mortem XDP prefrontal cortex (PFC) compared to control. Furthermore, there is a significant increase in histone H3 citrullination (H3R2R8R17cit3) with a concomitant increase in peptidylarginine deaminase 2 (PAD2) and 4 (PAD4), the enzymes catalyzing citrullination, in XDP post-mortem PFC. While there is a significant increase in myeloperoxidase (MPO) levels in XDP PFC, neutrophil elastase (NE) levels are not altered, suggesting that MPO may be released by activated microglia or reactive astrocytes in the brain. Similarly, there was an increase in H3R2R8R17cit3, PAD2 and PAD4 levels in XDP-derived fibroblasts. Importantly, treatment of fibroblasts with Cl-amidine, a pan inhibitor of PAD enzymes, reduced histone H3 citrullination and pro-inflammatory chemokine expression, without affecting cell survival. Taken together, our results demonstrate that inflammation is increased in XDP post-mortem brain and fibroblasts and unveil a new epigenetic potential therapeutic target.
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 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 | 0.000 | 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) | 0.001 | 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".