Nitrative stress activates JNK and decreases retrograde transport of brain‐derived neurotrophic factor in basal forebrain cholinergic neurons
Bibliographic record
Abstract
BACKGROUND: Basal forebrain cholinergic neurons (BFCNs) lose synapses and degenerate with age and in Alzheimer's disease (AD), contributing to cognitive decline. BFCNs rely on retrograde axonal transport of brain-derived neurotrophic factor (BDNF), a neurotrophin essential for synaptic plasticity, to maintain learning and memory. BDNF transport is decreased in aging, but the mechanisms are unclear. Elevated levels of nitrative stress occur in the aging brain and may promote BFCN neurodegeneration by interfering with BDNF retrograde transport. This study evaluates whether nitrative stress-induced activation of c-Jun N-terminal kinase (JNK) contributes to BDNF transport deficits. METHOD: Primary rat BFCNs cultured for 9 days in microfluidic chambers (creating fluidic isolation between axon terminals and cell bodies) were treated with either 1) a peroxynitrite donor (SIN-1) or 2) a combination of a JNK inhibitor (CC401) and SIN-1. JNK activation was measured by immunocytochemistry and BDNF axonal transport was assessed by adding Quantum dot-labelled BDNF to axon terminals followed by live cell fluorescence microscopy at the proximal axons (near cell bodies). RESULT: SIN-1 alone increased JNK activation and decreased BDNF transport, while inhibition of JNK with CC401 rescued SIN-1-associated transport deficits. CONCLUSION: This suggests that JNK activation by peroxynitrite may be a mechanism by which nitrative stress reduces BDNF transport in BFCNs. This study reveals that age-related nitrative stress may contribute to BDNF transport deficits, leading to BFCN degeneration and cognitive decline in 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) | 0.000 | 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) | 0.001 | 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".