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Basal exon skipping and genetic pleiotropy: A predictive model of disease pathogenesis

Overview of attention for article published in Science Translational Medicine, June 2015
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  • In the top 25% of all research outputs scored by Altmetric
  • High Attention Score compared to outputs of the same age (90th percentile)
  • Average Attention Score compared to outputs of the same age and source

Mentioned by

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27 X users
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2 patents

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73 Mendeley
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Article details
Title
Basal exon skipping and genetic pleiotropy: A predictive model of disease pathogenesis
Published in
Science Translational Medicine, June 2015
DOI 10.1126/scitranslmed.aaa5370
Pubmed ID
Authors
Abstract

Genetic pleiotropy, the phenomenon by which mutations in the same gene result in markedly different disease phenotypes, has proven difficult to explain with traditional models of disease pathogenesis. We have developed a model of pleiotropic disease that explains, through the process of basal exon skipping, how different mutations in the same gene can differentially affect protein production, with the total amount of protein produced correlating with disease severity. Mutations in the centrosomal protein of 290 kDa (CEP290) gene are associated with a spectrum of phenotypically distinct human diseases (the ciliopathies). Molecular biologic examination of CEP290 transcript and protein expression in cells from patients carrying CEP290 mutations, measured by quantitative polymerase chain reaction and Western blotting, correlated with disease severity and corroborated our model. We show that basal exon skipping may be the mechanism underlying the disease pleiotropy caused by CEP290 mutations. Applying our model to a different disease gene, CC2D2A (coiled-coil and C2 domains-containing protein 2A), we found that the same correlations held true. Our model explains the phenotypic diversity of two different inherited ciliopathies and may establish a new model for the pathogenesis of other pleiotropic human diseases.

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X Demographics

X Demographics

The data shown below were collected from the profiles of 27 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley demographics

Mendeley demographics

The data shown below were compiled from readership statistics for 73 Mendeley readers of this research output. Click here to see the associated Mendeley record.
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Geographical breakdown

Geographical breakdown
Country Count As %
United States 1 1%
Sweden 1 1%
United Kingdom 1 1%
Unknown 70 96%

Demographic breakdown

Readers by professional status
Readers by professional status Count As %
Researcher 15 21%
Student > Ph. D. Student 14 19%
Student > Master 7 10%
Professor > Associate Professor 6 8%
Student > Bachelor 4 5%
Other 9 12%
Unknown 18 25%
Readers by discipline
Readers by discipline Count As %
Agricultural and Biological Sciences 18 25%
Biochemistry, Genetics and Molecular Biology 16 22%
Medicine and Dentistry 13 18%
Neuroscience 4 5%
Psychology 1 1%
Other 3 4%
Unknown 18 25%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 18. 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 28 May 2019.
All research outputs
#2,225,102
of 26,966,971 outputs
Outputs from Science Translational Medicine
#2,986
of 5,279 outputs
Outputs of similar age
#26,072
of 281,041 outputs
Outputs of similar age from Science Translational Medicine
#60
of 117 outputs
Altmetric has tracked 26,966,971 research outputs across all sources so far. Compared to these this one has done particularly well and is in the 91st percentile: it's in the top 10% of all research outputs ever tracked by Altmetric.
So far Altmetric has tracked 5,279 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 93.1. This one is in the 43rd percentile – i.e., 43% of its peers scored the same or lower than it.
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 281,041 tracked outputs that were published within six weeks on either side of this one in any source. This one has done particularly well, scoring higher than 90% of its contemporaries.
We're also able to compare this research output to 117 others from the same source and published within six weeks on either side of this one. This one is in the 48th percentile – i.e., 48% of its contemporaries scored the same or lower than it.