Dataset Information

A mutation update on the LDS-associated genes TGFB2/3 and SMAD2/3.

ABSTRACT: The Loeys-Dietz syndrome (LDS) is a connective tissue disorder affecting the cardiovascular, skeletal, and ocular system. Most typically, LDS patients present with aortic aneurysms and arterial tortuosity, hypertelorism, and bifid/broad uvula or cleft palate. Initially, mutations in transforming growth factor-? (TGF-?) receptors (TGFBR1 and TGFBR2) were described to cause LDS, hereby leading to impaired TGF-? signaling. More recently, TGF-? ligands, TGFB2 and TGFB3, as well as intracellular downstream effectors of the TGF-? pathway, SMAD2 and SMAD3, were shown to be involved in LDS. This emphasizes the role of disturbed TGF-? signaling in LDS pathogenesis. Since most literature so far has focused on TGFBR1/2, we provide a comprehensive review on the known and some novel TGFB2/3 and SMAD2/3 mutations. For TGFB2 and SMAD3, the clinical manifestations, both of the patients previously described in the literature and our newly reported patients, are summarized in detail. This clearly indicates that LDS concerns a disorder with a broad phenotypical spectrum that is still emerging as more patients will be identified. All mutations described here are present in the corresponding Leiden Open Variant Database.

SUBMITTER: Schepers D

PROVIDER: S-EPMC5947146 | biostudies-literature | 2018 May

REPOSITORIES: biostudies-literature

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A mutation update on the LDS-associated genes TGFB2/3 and SMAD2/3.

Schepers Dorien D Tortora Giada G Morisaki Hiroko H MacCarrick Gretchen G Lindsay Mark M Liang David D Mehta Sarju G SG Hague Jennifer J Verhagen Judith J van de Laar Ingrid I Wessels Marja M Detisch Yvonne Y van Haelst Mieke M Baas Annette A Lichtenbelt Klaske K Braun Kees K van der Linde Denise D Roos-Hesselink Jolien J McGillivray George G Meester Josephina J Maystadt Isabelle I Coucke Paul P El-Khoury Elie E Parkash Sandhya S Diness Birgitte B Risom Lotte L Scurr Ingrid I Hilhorst-Hofstee Yvonne Y Morisaki Takayuki T Richer Julie J Désir Julie J Kempers Marlies M Rideout Andrea L AL Horne Gabrielle G Bennett Chris C Rahikkala Elisa E Vandeweyer Geert G Alaerts Maaike M Verstraeten Aline A Dietz Hal H Van Laer Lut L Loeys Bart B

Human mutation 20180306 5

The Loeys-Dietz syndrome (LDS) is a connective tissue disorder affecting the cardiovascular, skeletal, and ocular system. Most typically, LDS patients present with aortic aneurysms and arterial tortuosity, hypertelorism, and bifid/broad uvula or cleft palate. Initially, mutations in transforming growth factor-β (TGF-β) receptors (TGFBR1 and TGFBR2) were described to cause LDS, hereby leading to impaired TGF-β signaling. More recently, TGF-β ligands, TGFB2 and TGFB3, as well as intracellular down ...[more]

PMID: 29392890

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Project description:High-dimensional biological data collection across heterogeneous groups of samples has become increasingly common, creating high demand for dimensionality reduction techniques that capture underlying structure of the data. Discovering low-dimensional embeddings that describe the separation of any underlying discrete latent structure in data is an important motivation for applying these techniques since these latent classes can represent important sources of unwanted variability, such as batch effects, or interesting sources of signal such as unknown cell types. The features that define this discrete latent structure are often hard to identify in high-dimensional data. Principal component analysis (PCA) is one of the most widely used methods as an unsupervised step for dimensionality reduction. This reduction technique finds linear transformations of the data which explain total variance. When the goal is detecting discrete structure, PCA is applied with the assumption that classes will be separated in directions of maximum variance. However, PCA will fail to accurately find discrete latent structure if this assumption does not hold. Visualization techniques, such as t-Distributed Stochastic Neighbor Embedding (t-SNE) and Uniform Manifold Approximation and Projection (UMAP), attempt to mitigate these problems with PCA by creating a low-dimensional space where similar objects are modeled by nearby points in the low-dimensional embedding and dissimilar objects are modeled by distant points with high probability. However, since t-SNE and UMAP are computationally expensive, often a PCA reduction is done before applying them which makes it sensitive to PCAs downfalls. Also, tSNE is limited to only two or three dimensions as a visualization tool, which may not be adequate for retaining discriminatory information. The linear transformations of PCA are preferable to non-linear transformations provided by methods like t-SNE and UMAP for interpretable feature weights. Here, we propose iterative discriminant analysis (iDA), a dimensionality reduction technique designed to mitigate these limitations. iDA produces an embedding that carries discriminatory information which optimally separates latent clusters using linear transformations that permit post hoc analysis to determine features that define these latent structures.

Dataset Information

A mutation update on the LDS-associated genes TGFB2/3 and SMAD2/3.

Publications

A mutation update on the LDS-associated genes TGFB2/3 and SMAD2/3.

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