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Mutation-induced LZTR1 polymerization provokes cardiac pathology in recessive Noonan syndrome

Cited 4 time in Web of Science Cited 4 time in Scopus
Authors

Busley, Alexandra Viktoria; Gutierrez-Gutierrez, Oscar; Hammer, Elke; Koitka, Fabian; Mirzaiebadizi, Amin; Steinegger, Martin; Pape, Constantin; Boehmer, Linda; Schroeder, Henning; Kleinsorge, Mandy; Engler, Melanie; Cirstea, Ion Cristian; Gremer, Lothar; Willbold, Dieter; Altmuller, Janine; Marbach, Felix; Hasenfuss, Gerd; Zimmermann, Wolfram-Hubertus; Ahmadian, Mohammad Reza; Wollnik, Bernd; Cyganek, Lukas

Issue Date
2024-07
Publisher
Cell Press
Citation
Cell Reports, Vol.43 No.7, p. 114448
Abstract
Noonan syndrome patients harboring causative variants in LZTR1 are particularly at risk to develop severe and early-onset hypertrophic cardiomyopathy. In this study, we investigate the mechanistic consequences of a homozygous variant LZTR1 L580P by using patient-specific and CRISPR-Cas9-corrected induced pluripotent stem cell (iPSC) cardiomyocytes. Molecular, cellular, and functional phenotyping in combination with in silico prediction identify an LZTR1 L580P-specific disease mechanism provoking cardiac hypertrophy. The variant is predicted to alter the binding affinity of the dimerization domains facilitating the formation of linear LZTR1 polymers. LZTR1 complex dysfunction results in the accumulation of RAS GTPases, thereby provoking global pathological changes of the proteomic landscape ultimately leading to cellular hypertrophy. Furthermore, our data show that cardiomyocyte-specific MRAS degradation is mediated by LZTR1 via non-proteasomal pathways, whereas RIT1 degradation is mediated by both LZTR1-dependent and LZTR1-independent pathways. Uni- or biallelic genetic correction of the LZTR1 L580P missense variant rescues the molecular and cellular disease phenotype, providing proof of concept for CRISPR-based therapies.
ISSN
2211-1247
URI
https://hdl.handle.net/10371/209078
DOI
https://doi.org/10.1016/j.celrep.2024.114448
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  • College of Natural Sciences
  • School of Biological Sciences
Research Area Development of algorithms to search, cluster and assemble sequence data, Metagenomic analysis, Pathogen detection in sequencing data

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