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The emerging role of viral vectors as vehicles for DMD gene editing

Overview of attention for article published in Genome Medicine, May 2016
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About this Attention Score

  • Above-average Attention Score compared to outputs of the same age (51st percentile)

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Title
The emerging role of viral vectors as vehicles for DMD gene editing
Published in
Genome Medicine, May 2016
DOI 10.1186/s13073-016-0316-x
Pubmed ID
Authors

Ignazio Maggio, Xiaoyu Chen, Manuel A. F. V. Gonçalves

Abstract

Duchenne muscular dystrophy (DMD) is a genetic disorder caused by mutations in the dystrophin-encoding DMD gene. The DMD gene, spanning over 2.4 megabases along the short arm of the X chromosome (Xp21.2), is the largest genetic locus known in the human genome. The size of DMD, combined with the complexity of the DMD phenotype and the extent of the affected tissues, begs for the development of novel, ideally complementary, therapeutic approaches. Genome editing based on the delivery of sequence-specific programmable nucleases into dystrophin-defective cells has recently enriched the portfolio of potential therapies under investigation. Experiments involving different programmable nuclease platforms and target cell types have established that the application of genome-editing principles to the targeted manipulation of defective DMD loci can result in the rescue of dystrophin protein synthesis in gene-edited cells. Looking towards translation into the clinic, these proof-of-principle experiments have been swiftly followed by the conversion of well-established viral vector systems into delivery agents for DMD editing. These gene-editing tools consist of zinc-finger nucleases (ZFNs), engineered homing endoculeases (HEs), transcription activator-like effector nucleases (TALENs), and RNA-guided nucleases (RGNs) based on clustered, regularly interspaced, short palindromic repeats (CRISPR)-Cas9 systems. Here, we succinctly review these fast-paced developments and technologies, highlighting their relative merits and potential bottlenecks, when used as part of in vivo and ex vivo gene-editing strategies.

X Demographics

X Demographics

The data shown below were collected from the profiles of 4 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 94 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 94 100%

Demographic breakdown

Readers by professional status Count As %
Student > Master 20 21%
Researcher 16 17%
Student > Ph. D. Student 15 16%
Student > Bachelor 12 13%
Student > Postgraduate 3 3%
Other 9 10%
Unknown 19 20%
Readers by discipline Count As %
Biochemistry, Genetics and Molecular Biology 34 36%
Agricultural and Biological Sciences 15 16%
Medicine and Dentistry 6 6%
Social Sciences 4 4%
Pharmacology, Toxicology and Pharmaceutical Science 3 3%
Other 10 11%
Unknown 22 23%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 3. 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 08 June 2016.
All research outputs
#13,413,333
of 24,010,679 outputs
Outputs from Genome Medicine
#1,224
of 1,483 outputs
Outputs of similar age
#164,489
of 338,304 outputs
Outputs of similar age from Genome Medicine
#30
of 31 outputs
Altmetric has tracked 24,010,679 research outputs across all sources so far. This one is in the 44th percentile – i.e., 44% of other outputs scored the same or lower than it.
So far Altmetric has tracked 1,483 research outputs from this source. They typically receive a lot more attention than average, with a mean Attention Score of 26.6. This one is in the 17th percentile – i.e., 17% 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 338,304 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 51% of its contemporaries.
We're also able to compare this research output to 31 others from the same source and published within six weeks on either side of this one. This one is in the 6th percentile – i.e., 6% of its contemporaries scored the same or lower than it.