Nuclear Factor κB Signaling Regulates Neuronal Morphology and Cocaine Reward
Bibliographic record
Abstract
Although chronic cocaine-induced changes in dendritic spines on nucleus accumbens (NAc) neurons have been correlated with behavioral sensitization, the molecular pathways governing these structural changes, and their resulting behavioral effects, are poorly understood. The transcription factor, nuclear factor kappa B (NFkappaB), is rapidly activated by diverse stimuli and regulates expression of many genes known to maintain cell structure. Therefore, we evaluated the role of NFkappaB in regulating cocaine-induced dendritic spine changes on medium spiny neurons of the NAc and the rewarding effects of cocaine. We show that chronic cocaine induces NFkappaB-dependent transcription in the NAc of NFkappaB-Lac transgenic mice. This induction of NFkappaB activity is accompanied by increased expression of several NFkappaB genes, the promoters of which show chromatin modifications after chronic cocaine exposure consistent with their transcriptional activation. To study the functional significance of this induction, we used viral-mediated gene transfer to express either a constitutively active or dominant-negative mutant of Inhibitor of kappa B kinase (IKKca or IKKdn), which normally activates NFkappaB signaling, in the NAc. We found that activation of NFkappaB by IKKca increases the number of dendritic spines on NAc neurons, whereas inhibition of NFkappaB by IKKdn decreases basal dendritic spine number and blocks the increase in dendritic spines after chronic cocaine. Moreover, inhibition of NFkappaB blocks the rewarding effects of cocaine and the ability of previous cocaine exposure to increase an animal's preference for cocaine. Together, these studies establish a direct role for NFkappaB pathways in the NAc to regulate structural and behavioral plasticity to cocaine.
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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".