ArticleExperimental and molecular pathology2016
Genomic landscapes of endogenous retroviruses unveil intricate genetics of conventional and genetically-engineered laboratory mouse strains.
Article in Experimental and molecular pathology, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
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5 authors at 1 institution in 1 country.
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Abstract
Laboratory strains of mice, both conventional and genetically engineered, have been introduced as critical components of a broad range of studies investigating normal and disease biology. Currently, the genetic identity of laboratory mice is primarily confirmed by surveying polymorphisms in selected sets of "conventional" genes and/or microsatellites in the absence of a single completely sequenced mouse genome. First, we examined variations in the genomic landscapes of transposable repetitive elements, named the TREome, in conventional and genetically engineered mouse strains using murine leukemia virus-type endogenous retroviruses (MLV-ERVs) as a probe. A survey of the genomes from 56 conventional strains revealed strain-specific TREome landscapes, and certain families (e.g., C57BL) of strains were discernible with defined patterns. Interestingly, the TREome landscapes of C3H/HeJ (toll-like receptor-4 [TLR4] mutant) inbred mice were different from its control C3H/HeOuJ (TLR4 wild-type) strain. In addition, a CD14 knock-out strain had a distinct TREome landscape compared to its control/backcross C57BL/6J strain. Second, an examination of superantigen (SAg, a "TREome gene") coding sequences of mouse mammary tumor virus-type ERVs in the genomes of the 46 conventional strains revealed a high diversity, suggesting a potential role of SAgs in strain-specific immune phenotypes. The findings from this study indicate that unexplored and intricate genomic variations exist in laboratory mouse strains, both conventional and genetically engineered. The TREome-based high-resolution genetics surveillance system for laboratory mice would contribute to efficient study design with quality control and accurate data interpretation. This genetics system can be easily adapted to other species ranging from plants to humans.
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