P3‐377: A novel pathogenic pathway of immune activation detectable before cinical onset in Huntington's disease
Bibliographic record
Abstract
Huntington's disease (HD) is an inherited neurodegenerative disorder characterized by both neurological and systemic abnormalities. We previously demonstrated evidence of immune activation in peripheral plasma in manifest HD using proteomic profiling but no significant differences between controls and premanifest mutation carriers have previously been shown and the nature and pathogenic relevance of the immune activation in HD remains incompletely explored. We quantified levels of key inflammatory and immunomodulatory molecules in human plasma and serum from 3 different mouse models of HD using multiplex ELISA. We used QT-PCR to examine huntingtin expression in HD monocytes and cytokines in HD striatum and performed a functional study of HD macrophages. We found widespread evidence of innate immune activation detectable in plasma throughout the course of HD. IL-6 levels were increased in HD gene carriers with a mean of 16 years before the predicted onset of clinical symptoms. Monocytes expressed mutant huntingtin in HD and macrophages from an HD mouse model were constitutively overactive. The cerebrospinal fluid and striatum of HD patients exhibited a similar immune activation. Our results suggest that inflammatory changes detected in peripheral plasma may be biologically relevant and mirror the neurodegenerative process occurring in the CNS. Remarkably, peripheral inflammatory changes may also reveal early pathogenic events in HD, occurring over 15 years before the onset of neurological manifestations. The inflammatory changes seen in patients are echoed in mouse models of HD. Importantly, they may therefore provide translational biomarkers for the use of HD mouse models in the development of therapeutic interventions.
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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 | 0.000 | 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".