Excessive Laughter-like Vocalizations, Microcephaly, and Translational Outcomes in the<i>Ube3a</i>Deletion Rat Model of Angelman Syndrome
Bibliographic record
Abstract
Angelman syndrome (AS) is a rare genetic neurodevelopmental disorder characterized by intellectual disabilities, motor and balance deficits, impaired communication, and a happy, excitable demeanor with frequent laughter. We sought to elucidate a preclinical outcome measure in male and female rats that addressed communication abnormalities of AS and other neurodevelopmental disorders in which communication is atypical and/or lack of speech is a core feature. We discovered, and herein report for the first time, excessive laughter-like 50 kHz ultrasonic emissions in theUbe3amat–/pat+rat model of AS, which suggests an excitable, playful demeanor and elevated positive affect, similar to the demeanor of individuals with AS. Also in line with the AS phenotype,Ube3amat–/pat+rats demonstrated aberrant social interactions with a novel partner, distinctive gait abnormalities, impaired cognition, an underlying LTP deficit, and profound reductions in brain volume. These unique, robust phenotypes provide advantages compared with currently available mouse models and will be highly valuable as outcome measures in the evaluation of therapies for AS. SIGNIFICANCE STATEMENTAngelman syndrome (AS) is a severe neurogenetic disorder for which there is no cure, despite decades of research using mouse models. This study used a recently developed rat model of AS to delineate disease-relevant outcome measures to facilitate therapeutic development. We found the rat to be a strong model of AS, offering several advantages over mouse models by exhibiting numerous AS-relevant phenotypes, including overabundant laughter-like vocalizations, reduced hippocampal LTP, and volumetric anomalies across the brain. These findings are unconfounded by detrimental motor abilities and background strain, issues plaguing mouse models. This rat model represents an important advancement in the field of AS, and the outcome metrics reported herein will be central to the therapeutic pipeline.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| 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".