↓ Skip to main content

Transcriptional Signature of an Altered Purine Metabolism in the Skeletal Muscle of a Huntington's Disease Mouse Model

Overview of attention for article published in Frontiers in Physiology, March 2017
Altmetric Badge

About this Attention Score

  • In the top 25% of all research outputs scored by Altmetric
  • Good Attention Score compared to outputs of the same age (79th percentile)
  • High Attention Score compared to outputs of the same age and source (81st percentile)

Mentioned by

news
1 news outlet
twitter
1 X user

Citations

dimensions_citation
22 Dimensions

Readers on

mendeley
26 Mendeley
You are seeing a free-to-access but limited selection of the activity Altmetric has collected about this research output. Click here to find out more.
Title
Transcriptional Signature of an Altered Purine Metabolism in the Skeletal Muscle of a Huntington's Disease Mouse Model
Published in
Frontiers in Physiology, March 2017
DOI 10.3389/fphys.2017.00127
Pubmed ID
Authors

Michal Mielcarek, Ryszard T. Smolenski, Mark Isalan

Abstract

Huntington's disease (HD) is a fatal neurodegenerative disorder, caused by a polyglutamine expansion in the huntingtin protein (HTT). HD has a peripheral component to its pathology: skeletal muscles are severely affected, leading to atrophy, and malfunction in both pre-clinical and clinical settings. We previously used two symptomatic HD mouse models to demonstrate the impairment of the contractile characteristics of the hind limb muscles, which was accompanied by a significant loss of function of motor units. The mice displayed a significant reduction in muscle force, likely because of deteriorations in energy metabolism, decreased oxidation, and altered purine metabolism. There is growing evidence suggesting that HD-related skeletal muscle malfunction might be partially or completely independent of CNS degeneration. The pathology might arise from mutant HTT within muscle (loss or gain of function). Hence, it is vital to identify novel peripheral biomarkers that will reflect HD skeletal muscle atrophy. These will be important for upcoming clinical trials that may target HD peripherally. In order to identify potential biomarkers that might reflect muscle metabolic changes, we used qPCR to validate key gene transcripts in different skeletal muscle types. Consequently, we report a number of transcript alterations that are linked to HD muscle pathology.

X Demographics

X Demographics

The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
As of 1 July 2024, you may notice a temporary increase in the numbers of X profiles with Unknown location. Click here to learn more.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 26 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 26 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 6 23%
Student > Ph. D. Student 4 15%
Student > Master 3 12%
Student > Bachelor 2 8%
Librarian 2 8%
Other 5 19%
Unknown 4 15%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 10 38%
Agricultural and Biological Sciences 2 8%
Medicine and Dentistry 2 8%
Nursing and Health Professions 1 4%
Neuroscience 1 4%
Other 2 8%
Unknown 8 31%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 10. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 05 May 2017.
All research outputs
#3,222,122
of 22,958,253 outputs
Outputs from Frontiers in Physiology
#1,730
of 13,712 outputs
Outputs of similar age
#61,933
of 310,726 outputs
Outputs of similar age from Frontiers in Physiology
#40
of 221 outputs
Altmetric has tracked 22,958,253 research outputs across all sources so far. Compared to these this one has done well and is in the 85th percentile: it's in the top 25% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 13,712 research outputs from this source. They typically receive more attention than average, with a mean Attention Score of 7.6. This one has done well, scoring higher than 87% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 310,726 tracked outputs that were published within six weeks on either side of this one in any source. This one has done well, scoring higher than 79% of its contemporaries.
We're also able to compare this research output to 221 others from the same source and published within six weeks on either side of this one. This one has done well, scoring higher than 81% of its contemporaries.