Hypothalamic AAV-BDNF gene therapy improves metabolic function and behavior in the Magel2-null mouse model of Prader-Willi syndrome
Bibliographic record
Abstract
Individuals with Prader-Willi syndrome (PWS) display developmental delays, cognitive impairment, excessive hunger, obesity, and various behavioral abnormalities. Current PWS treatments are limited to strict supervision of food intake and growth hormone therapy, highlighting the need for new therapeutic strategies. Brain-derived neurotrophic factor (BDNF) functions downstream of hypothalamic feeding circuitry and has roles in energy homeostasis and behavior. In this preclinical study, we assessed the translational potential of hypothalamic adeno-associated virus (AAV)-BDNF gene therapy as a therapeutic for metabolic dysfunction in the Magel2 -null mouse model of PWS. To facilitate clinical translation, our BDNF vector included an autoregulatory element allowing for transgene titration in response to the host's physiological needs. Hypothalamic BDNF gene transfer prevented weight gain, decreased fat mass, increased lean mass, and increased relative energy expenditure in female Magel2 -null mice. Moreover, BDNF gene therapy improved glucose metabolism, insulin sensitivity, and circulating adipokine levels. Metabolic improvements were maintained through 23 weeks with no adverse behavioral effects, indicating high levels of efficacy and safety. Male Magel2 -null mice also responded positively to BDNF gene therapy, displaying improved body composition, insulin sensitivity, and glucose metabolism. Together, these data suggest that regulating hypothalamic BDNF could be effective in the treatment of PWS-related metabolic abnormalities.
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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 | 0.000 |
| 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 | 0.000 | 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".