NUDT6, the Antisense Protein of FGF2 Gene, Plays a Depressogenic Role by Promoting Inflammation and Suppressing Neurogenesis without Altering FGF2 Signaling
Bibliographic record
Abstract
Abstract Fibroblast growth factor-2 (FGF2) is involved in the regulation of affective behavior and shows antidepressant effects through Akt and ERK1/2 pathways. NUDT6 is a protein encoded from FGF2 gene’s antisense strand and its role in the regulation of affective behavior is unclear. Here, we show that increasing NUDT6 expression in the hippocampus results in depression-like behavior in rats without changing FGF2 levels or activating its downstream effectors, Akt and ERK1/2. Instead, NUDT6 acts by inducing inflammatory signaling, specifically by increasing S100A9 levels, activating NF-κB and rising microglia number along with a reduction in neurogenesis. Conversely, inhibition of hippocampal NUDT6 expression by shRNA results in antidepressant effects and increases neurogenesis without altering FGF2 levels. Together these findings suggest that NUDT6 may play a role in major depression by inducing a proinflammatory state and serve as a novel therapeutic target for antidepressant development. This is the first report of an antisense protein acting through a different mechanism of action than regulation of its sense protein. The opposite effects of NUDT6 and FGF2 on depression-like behavior may serve as a mechanism to fine-tune affective behavior. Our findings open up new venues for studying the differential regulation and functional interactions of sense and antisense proteins in neural function and behavior as well as in neuropsychiatric disorders.
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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) | 0.002 | 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".