Nilotinib Improves Bioenergetic Profiling in Brain Astroglia in the 3xTg Mouse Model of Alzheimer’s Disease
Bibliographic record
Abstract
Current treatments targeting amyloid beta in Alzheimer's disease (AD) have minimal efficacy, which results in a huge unmet medical need worldwide. Accumulating data suggest that brain mitochondrial dysfunction play a critical role in AD pathogenesis. Targeting cellular mechanisms associated with mitochondrial dysfunction in AD create a novel approach for drug development. This study investigated the effects of nilotinib, as a selective tyrosine kinase inhibitor, in astroglia derived from 3xTg-AD mice versus their C57BL/6-controls. Parameters included oxygen consumption rates (OCR), ATP, cytochrome c oxidase (COX), citrate synthase (CS) activity, alterations in oxidative phosphorylation (OXPHOS), nuclear factor kappa B (NF-κB), key regulators of mitochondrial dynamics (mitofusin (Mfn1), dynamin-related protein 1 (Drp1)), and mitochondrial biogenesis (peroxisome proliferator-activated receptor gamma coactivator1-alpha (PGC-1α), calcium/calmodulin-dependent protein kinase II (CaMKII), and nuclear factor (erythroid-derived 2)-like 2 (Nrf2)). Nilotinib increased OCR, ATP, COX, Mfn1, and OXPHOS levels in 3xTg astroglia. No significant differences were detected in levels of Drp1 protein and CS activity. Nilotinib enhanced mitochondrial numbers, potentially through a CaMKII-PGC1α-Nrf2 pathway in 3xTg astroglia. Additionally, nilotinib-induced OCR increases were reduced in the presence of the NF-κB inhibitor, Bay11-7082. The data suggest that NF-κB signaling is intimately involved in nilotinib-induced changes in bioenergetics in 3xTg brain astroglia. Nilotinib increased translocation of the NF-κB p50 subunit into the nucleus of 3xTg astroglia that correlates with an increased expression and activation of NF-κB. The current findings support a role for nilotinib in improving mitochondrial function and suggest that astroglia may be a key therapeutic target in treating AD.
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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 | 0.000 |
| 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 | 0.000 |
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".