Restoration neurite growth by removing the blockage of endosome trafficking in Alzheimer-like mice
Bibliographic record
Abstract
Synaptic loss is strongly associated with cognitive decline in Alzheimer's disease (AD). Endosomal trafficking dysfunction, observed in AD brains, impairs neurite growth. Because endosomal trafficking is essential for synaptic development, we selected LMTK1, a negative regulator of Rab11/RE pathway, for this study, given its upregulation in AD models. Clinical genomic data from the ADNI (Alzheimer's Disease Neuroimaging Initiative) database were analyzed to evaluate the relationship between LMTK1 and AD. Two AD mouse models, 3xTg and SAMP8, were examined for neurite outgrowth, synaptic density, LMTK1 expression, and recycling endosomes (RE) transport. LMTK1 knockdown was achieved using AAV. The Morris water maze, Golgi staining, immunofluorescence, and electrophysiology experiments were used to assess cognitive function, neurite outgrowth, synaptic density, RE transport, long-term potentiation (LTP), and synaptic transmission. The mechanism of LMTK1 in regulating RE transport was examined through co-immunoprecipitation, proteomics, and point mutation experiments. This study shows that phosphorylated LMTK1 activates TBC1D9B, which deactivates Rab11a and may suppress Rab11a + endosome trafficking and neurite growth. Clinical genomics data from the ADNI database support LMTK1's involvement in cognition in AD and possibly in glucose hypometabolism related to synaptic dysfunction. Knocking down LMTK1 improves neurite atrophy and synaptic density loss, likely by enhancing Rab11 + endosome transport. Restoration of neurite morphology, hippocampal LTP, and cognitive function in AD mice suggest that inhibiting LMTK1 could represent a novel therapy for promoting neurite growth in AD. Hyperphosphorylation of LMTK1 may induce RE transport dysfunction, leading to neurite atrophy in AD mice. Therefore, targeting LMTK1 may offer a promising therapeutic approach for AD therapy.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".