<i>C9orf72</i> dipeptides activate the NLRP3 inflammasome
Bibliographic record
Abstract
Abstract Frontotemporal dementia and amyotrophic lateral sclerosis are neurodegenerative diseases with considerable clinical, genetic and pathological overlap. The most common cause of both diseases is a hexanucleotide repeat expansion in C9orf72. The expansion is translated to produce five toxic dipeptides, which aggregate in patient brain. Neuroinflammation is a feature of frontotemporal dementia and amyotrophic lateral sclerosis; however, its causes are unknown. The nod-like receptor family, pyrin domain-containing 3 inflammasome is implicated in several other neurodegenerative diseases as a driver of damaging inflammation. The inflammasome is a multi-protein complex which forms in immune cells in response to tissue damage, pathogens or aggregating proteins. Inflammasome activation is observed in models of other neurodegenerative diseases such as Alzheimer’s disease, and inflammasome inhibition rescues cognitive decline in rodent models of Alzheimer’s disease. Here, we show that a dipeptide arising from the C9orf72 expansion, poly-glycine–arginine, activated the inflammasome in microglia and macrophages, leading to secretion of the pro-inflammatory cytokine, interleukin-1β. Poly-glycine–arginine also activated the inflammasome in organotypic hippocampal slice cultures, and immunofluorescence imaging demonstrated formation of inflammasome specks in response to poly-glycine–arginine. Several clinically available anti-inflammatory drugs rescued poly-glycine–arginine-induced inflammasome activation. These data suggest that C9orf72 dipeptides contribute to the neuroinflammation observed in patients, and highlight the inflammasome as a potential therapeutic target for frontotemporal dementia and amyotrophic lateral sclerosis.
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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 | 0.001 | 0.001 |
| 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".