Differential roles of <scp>NR4A2</scp> ( <scp>NURR1</scp> ) paralogs in the brain and behavior of zebrafish
Bibliographic record
Abstract
Dopaminergic (DAnergic) dysfunction and imbalanced dopamine (DA) levels are known contributors to the pathogenesis of numerous psychiatric and neurodegenerative disorders. Of the many identified risk factors for DA-associated disorders, nuclear receptor subfamily 4 group A2 (NR4A2; or nuclear receptor related-1 protein (NURR1)), a transcription factor involved in DAnergic differentiation, has been associated with Parkinson's disease and attention deficit hyperactive disorder (ADHD). In zebrafish, transient loss of nr4a2 was previously shown to decrease tyrosine hydroxylase (TH) expression and impair locomotion. To further characterize the roles of the two zebrafish nr4a2 paralogs, nr4a2a, and nr4a2b, we produced targeted loss-of-function mutants and examined DAnergic neuron regeneration, oxidative respiration, and behavioral traits. The loss of nr4a2a function more closely recapitulated Parkinsonian phenotypes and affected neurotrophic factor gene expression. Conversely, nr4a2b mutants displayed behavioral symptoms reminiscent of mice deficient in Nr4a2 with increased neurotrophic output. In contrast, nr4a2b mutants also displayed increased metabolic input from non-mitochondrial sources indicative of high cytosolic reactive oxygen species and perturbed mitochondrial function. The nr4a2a mutants also showed increased maximal respiration, which may suggest a compensatory mechanism to meet the metabolic requirements of DAnergic neuron health. Overall, the zebrafish mutants generated in this study helped uncover molecular mechanisms involved in DA-related disease pathologies, and in the regeneration of DAnergic neurons.
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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) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".