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Autism spectrum disorders (ASDs) arise from altered development of the central nervous system, and manifest behaviorally as social interaction deficits and restricted and repetitive behaviors. Alterations to parvalbumin (PV) expressing interneurons have been implicated in the neuropathological and behavioral deficits in autism. In addition, perineuronal nets (PNNs), specialized extracellular matrix structures that enwrap the PV-expressing neurons, also may be altered, which compromises neuronal function and susceptibility to oxidative stress. In particular, the prefrontal cortex (PFC), which regulates several core autistic traits, relies on the normal organization of PNNs and PV-expressing cells, as well as other neural circuit elements. Consequently, we investigated whether PNNs and PV-expressing cells were altered in the PFC of the CNTNAP2 knockout mouse model of ASD and whether these contributed to core autistic-like behaviors in this model system. We observed an overexpression of PNNs, PV-expressing cells, and PNNs enwrapping PV-expressing cells in adult CNTNAP2 mice. Transient digestion of PNNs from the prefrontal cortex (PFC) by injection of chondroitinase ABC in CNTNAP2 mutant mice rescued some of the social interaction deficits, but not the restricted and repetitive behaviors. These findings suggest that the neurobiological regulation of PNNs and PVs in the PFC contribute to social interaction behaviors in neurological disorders including autism.
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Deep phenotyping reveals movement phenotypes in mouse neurodevelopmental models
Abstract BackgroundRepetitive action, resistance to environmental change and fine motor disruptions are hallmarks of autism spectrum disorder (ASD) and other neurodevelopmental disorders, and vary considerably from individual to individual. In animal models, conventional behavioral phenotyping captures such fine-scale variations incompletely. Here we observed male and female C57BL/6J mice to methodically catalog adaptive movement over multiple days and examined two rodent models of developmental disorders against this dynamic baseline. We then investigated the behavioral consequences of a cerebellum-specific deletion in Tsc1 protein and a whole-brain knockout in Cntnap2 protein in mice. Both of these mutations are found in clinical conditions and have been associated with ASD. MethodsWe used advances in computer vision and deep learning, namely a generalized form of high-dimensional statistical analysis, to develop a framework for characterizing mouse movement on multiple timescales using a single popular behavioral assay, the open-field test. The pipeline takes virtual markers from pose estimation to find behavior clusters and generate wavelet signatures of behavior classes. We measured spatial and temporal habituation to a new environment across minutes and days, different types of self-grooming, locomotion and gait. ResultsBoth Cntnap2 knockouts and L7-Tsc1 mutants showed forelimb lag during gait. L7-Tsc1 mutants and Cntnap2 knockouts showed complex defects in multi-day adaptation, lacking the tendency of wild-type mice to spend progressively more time in corners of the arena. In L7-Tsc1 mutant mice, failure to adapt took the form of maintained ambling, turning and locomotion, and an overall decrease in grooming. However, adaptation in these traits was similar between wild-type mice and Cntnap2 knockouts. L7-Tsc1 mutant and Cntnap2 knockout mouse models showed different patterns of behavioral state occupancy. LimitationsGenetic risk factors for autism are numerous, and we tested only two. Our pipeline was only done under conditions of free behavior. Testing under task or social conditions would reveal more information about behavioral dynamics and variability. ConclusionsOur automated pipeline for deep phenotyping successfully captures model-specific deviations in adaptation and movement as well as differences in the detailed structure of behavioral dynamics. The reported deficits indicate that deep phenotyping constitutes a robust set of ASD symptoms that may be considered for implementation in clinical settings as quantitative diagnosis criteria.
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- Award ID(s):
- 1734030
- PAR ID:
- 10363727
- Publisher / Repository:
- Springer Science + Business Media
- Date Published:
- Journal Name:
- Molecular Autism
- Volume:
- 13
- Issue:
- 1
- ISSN:
- 2040-2392
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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