Early Detection of Apathetic Phenotypes in Huntington’s Disease Knock-in Mice Using Open Source Tools View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2018-12

AUTHORS

Shawn Minnig, Robert M. Bragg, Hardeep S. Tiwana, Wes T. Solem, William S. Hovander, Eva-Mari S. Vik, Madeline Hamilton, Samuel R. W. Legg, Dominic D. Shuttleworth, Sydney R. Coffey, Jeffrey P. Cantle, Jeffrey B. Carroll

ABSTRACT

Apathy is one of the most prevalent and progressive psychiatric symptoms in Huntington's disease (HD) patients. However, preclinical work in HD mouse models tends to focus on molecular and motor, rather than affective, phenotypes. Measuring behavior in mice often produces noisy data and requires large cohorts to detect phenotypic rescue with appropriate power. The operant equipment necessary for measuring affective phenotypes is typically expensive, proprietary to commercial entities, and bulky which can render adequately sized mouse cohorts as cost-prohibitive. Thus, we describe here a home-built, open-source alternative to commercial hardware that is reliable, scalable, and reproducible. Using off-the-shelf hardware, we adapted and built several of the rodent operant buckets (ROBucket) to test HttQ111/+ mice for attention deficits in fixed ratio (FR) and progressive ratio (PR) tasks. We find that, despite normal performance in reward attainment in the FR task, HttQ111/+ mice exhibit reduced PR performance at 9-11 months of age, suggesting motivational deficits. We replicated this in two independent cohorts, demonstrating the reliability and utility of both the apathetic phenotype, and these ROBuckets, for preclinical HD studies. More... »

PAGES

2304

Journal

TITLE

Scientific Reports

ISSUE

1

VOLUME

8

Author Affiliations

Identifiers

URI

http://scigraph.springernature.com/pub.10.1038/s41598-018-20607-7

DOI

http://dx.doi.org/10.1038/s41598-018-20607-7

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1100699230

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/29396492


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Download the RDF metadata as:  json-ld nt turtle xml License info

HOW TO GET THIS DATA PROGRAMMATICALLY:

JSON-LD is a popular format for linked data which is fully compatible with JSON.

curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1038/s41598-018-20607-7'

N-Triples is a line-based linked data format ideal for batch operations.

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1038/s41598-018-20607-7'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1038/s41598-018-20607-7'

RDF/XML is a standard XML format for linked data.

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1038/s41598-018-20607-7'


 

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