KIF9 Ameliorates Neuropathology and Cognitive Dysfunction by Promoting Macroautophagy in a Mouse Model of Alzheimer's Disease
Bibliographic record
Abstract
Alzheimer's disease (AD) is a prevalent neurodegenerative disorder affecting the elderly. The imbalance of protein production and degradation processes leads to the accumulation of misfolded and abnormally aggregated amyloid-beta (Aβ) in the extracellular space and forms senile plaques, which constitute one of the most critical pathological hallmarks of AD. KIF9, a member of the kinesin protein superfamily, mediates the anterograde transport of intracellular cargo along microtubules. However, the exact role of KIF9 in AD pathogenesis remains largely elusive. In this study, we reported that the expression of kinesin family member 9 (KIF9) in the hippocampus of APP23/PS45 double-transgenic AD model mice declined in an age-dependent manner, concurrent with macroautophagy dysfunction. Furthermore, we found that KIF9 mediated the transport of lysosomes through kinesin light chain 1 (KLC1), thereby participating in the degradation of amyloidogenic pathway-related proteins of Aβ precursor protein (APP) in AD model cells through promoting the macroautophagy pathway. Importantly, genetic upregulation of KIF9 via adeno-associated virus (AAV) diminished Aβ deposition and alleviated cognitive impairments in AD model mice by enhancing macroautophagy function. Collectively, our findings underscore the ability of KIF9 to promote macroautophagy through KLC1-mediated anterograde transport of lysosomes, effectively ameliorating cognitive dysfunction in AD model mice. These discoveries suggest that KIF9 may represent a novel therapeutic target for the treatment of AD.
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 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 | 0.000 | 0.000 |
| 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".