C9orf72 deficiency impairs the autophagic response to aggregated TDP-25 and exacerbates TDP-25-mediated neurodegeneration in vivo
Bibliographic record
Abstract
Abstract Cytoplasmic aggregates of the predominantly nuclear TAR DNA-binding protein 43 (TDP-43) are a pathological hallmark of amyotrophic lateral sclerosis (ALS) and frontotemporal dementia (FTD) cases caused by G4C2 hexanucleotide repeat expansions in C9orf72 (C9-ALS/FTD). While these repeat expansions are associated with both gain- and loss-of-function mechanisms, the contribution of C9orf72 loss of function to disease pathogenesis remains unclear. C9orf72 has been shown to regulate autophagy, and its deficiency has been shown to exacerbate phenotypes in gain-of-function G4C2 models, implicating impaired autophagic clearance in disease pathogenesis. Here, we directly test whether C9orf72 deficiency exacerbates TDP-43 pathology and neurodegeneration in vivo. Using AAV9-vectors to drive neuron-specific expression of pathologically relevant C-terminal species of TDP-43, TDP-35 and TDP-25, we established models of TDP-43 pathology that recapitulate key disease features, including cytoplasmic aggregates, motor and cognitive decline, and neuronal loss. TDP-25 expression in particular produced robust, abnormally phosphorylated, ubiquitinated and p62-labelled cytoplasmic aggregates, modelling TDP-43 pathology in disease. Loss of C9orf72 in TDP-25-expressing mice accelerated the onset of motor deficits, increased neurodegeneration, and impaired the autophagic response to TDP-25 expression. These findings reveal that C9orf72 deficiency disrupts autophagy and exacerbates TDP-25-mediated toxicity in vivo, supporting a contributory role for C9orf72 loss-of-function in driving neurodegeneration in C9-ALS/FTD.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".