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Author (up) Klee, E.W.; Zimmermann, M.T.
Title Molecular modeling of LDLR aids interpretation of genomic variants Type Journal Article
Year 2019 Publication Abbreviated Journal Journal of Molecular Medicine
Volume 97 Issue 4 Pages 533-540
Keywords gene:LDLR
Abstract Genetic variants in low-density lipoprotein receptor (LDLR) are known to cause familial hypercholesterolemia (FH), occurring in up to 1 in 200 people (Youngblom E. et al. 1993 and Nordestgaard BG et al. 34:3478–3490a, 2013) and leading to significant risk for heart disease. Clinical genomics testing using high-throughput sequencing is identifying novel genomic variants of uncertain significance (VUS) in individuals suspected of having FH, but for whom the causal link to the disease remains to be established (Nordestgaard BG et al. 34:3478–3490a, 2013). Unfortunately, experimental data about the atomic structure of the LDL binding domains of LDLR at extracellular pH does not exist. This leads to an inability to apply protein structure-based methods for assessing novel variants identified through genetic testing. Thus, the ambiguities in interpretation of LDLR variants are a barrier to achieving the expected clinical value for personalized genomics assays for management of FH. In this study, we integrated data from the literature and related cellular receptors to develop high-resolution models of full-length LDLR at extracellular conditions and use them to predict which VUS alter LDL binding. We believe that the functional effects of LDLR variants can be resolved using a combination of structural bioinformatics and functional assays, leading to a better correlation with clinical presentation. We have completed modeling of LDLR in two major physiologic conditions, generating detailed hypotheses for how each of the 1007 reported protein variants may affect function. • Hundreds of variants are observed in the LDLR, but most lack interpretation. • Molecular modeling is aided by biochemical knowledge. • We generated context-specific 3D protein models of LDLR. • Our models allowed mechanistic interpretation of many variants. • We interpreted both rare and common genomic variants in their physiologic context. • Effects of genomic variants are often context-specific.
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Series Volume Series Issue Edition
ISSN 0946-2716 ISBN Medium
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Call Number AG @ matthewjvarga @ Serial 47191
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