Absence of cardiovascular manifestations in a haploinsufficient Tgfbr1 mouse model

Loeys-Dietz syndrome (LDS) is an autosomal dominant arterial aneurysm disease belonging to the spectrum of transforming growth factor β (TGFβ)-associated vasculopathies. In its most typical form it is characterized by the presence of hypertelorism, bifid uvula/cleft palate and aortic aneurysm and/or arterial tortuosity. LDS is caused by heterozygous loss of function mutations in the genes encoding TGFβ receptor 1 and 2 (TGFBR1 and -2), which lead to a paradoxical increase in TGFβ signaling. To address this apparent paradox and to gain more insight into the pathophysiology of aneurysmal disease, we characterized a new Tgfbr1 mouse model carrying a p.Y378*nonsense mutation. Study of the natural history in this model showed that homozygous mutant mice die during embryonic development due to defective vascularization. Heterozygous mutant mice aged 6 and 12 months were morphologically and (immuno)histochemically indistinguishable from wild-type mice. We show that the mutant allele is degraded by nonsense mediated mRNA decay, expected to result in haploinsufficiency of the mutant allele. Since this haploinsufficiency model does not result in cardiovascular malformations, it does not allow further study of the process of aneurysm formation. In addition to providing a comprehensive method for cardiovascular phenotyping in mice, the results of this study confirm that haploinsuffciency is not the underlying genetic mechanism in human LDS

Querying large treebanks: Benchmarking GrETEL indexing

The amount of data that is available for research grows rapidly, yet technology to efficiently interpret and excavate these data lags behind. For instance, when using large treebanks for linguistic research, the speed of a query leaves much to be desired. GrETEL Indexing, or GrInding, tackles this issue. The idea behind GrInding is to make the search space as small as possible before actually starting the treebank search, by pre-processing the treebank at hand. We recursively divide the treebank into smaller parts, called subtree-banks, which are then converted into database files. All subtree-banks are organized according to their linguistic dependency pattern, and labeled as such. Additionally, general patterns are linked to more specific ones. By doing so, we create millions of databases, and given a linguistic structure we know in which databases that structure can occur, leading up to a significant efficiency boost. We present the results of a benchmark experiment, testing the effect of the GrInding procedure on the SoNaR-500 treebank.status: publishe