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Find video protocols related to scientific articles indexed in Pubmed.
Efficient bi-allelic gene knockout and site-specific knock-in mediated by TALENs in pigs.
Sci Rep
PUBLISHED: 06-10-2014
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Pigs are ideal organ donors for xenotransplantation and an excellent model for studying human diseases, such as neurodegenerative disease. Transcription activator-like effector nucleases (TALENs) are used widely for gene targeting in various model animals. Here, we developed a strategy using TALENs to target the GGTA1, Parkin and DJ-1 genes in the porcine genome using Large White porcine fibroblast cells without any foreign gene integration. In total, 5% (2/40), 2.5% (2/80), and 22% (11/50) of the obtained colonies of fibroblast cells were mutated for GGTA1, Parkin, and DJ-1, respectively. Among these mutant colonies, over 1/3 were bi-allelic knockouts (KO), and no off-target cleavage was detected. We also successfully used single-strand oligodeoxynucleotides to introduce a short sequence into the DJ-1 locus. Mixed DJ-1 mutant colonies were used as donor cells for somatic cell nuclear transfer (SCNT), and three female piglets were obtained (two were bi-allelically mutated, and one was mono-allelically mutated). Western blot analysis showed that the expression of the DJ-1 protein was disrupted in KO piglets. These results imply that a combination of TALENs technology with SCNT can efficiently generate bi-allelic KO pigs without the integration of exogenous DNA. These DJ-1 KO pigs will provide valuable information for studying Parkinson's disease.
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JoVE Visualize is a tool created to match the last 5 years of PubMed publications to methods in JoVE's video library.

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We use abstracts found on PubMed and match them to JoVE videos to create a list of 10 to 30 related methods videos.

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In developing our video relationships, we compare around 5 million PubMed articles to our library of over 4,500 methods videos. In some cases the language used in the PubMed abstracts makes matching that content to a JoVE video difficult. In other cases, there happens not to be any content in our video library that is relevant to the topic of a given abstract. In these cases, our algorithms are trying their best to display videos with relevant content, which can sometimes result in matched videos with only a slight relation.