Predictive Modeling and Structure Analysis of Genetic Variants in Familial Hypercholesterolemia: Implications for Diagnosis and Protein Interaction Studies
dc.contributor.author | Larrea Sebal, Asier | |
dc.contributor.author | Jebari Benslaiman, Shifa | |
dc.contributor.author | Galicia García, Unai | |
dc.contributor.author | San José Urteaga, Ane | |
dc.contributor.author | Belloso Uribe, Kepa | |
dc.contributor.author | Benito Vicente, Asier | |
dc.contributor.author | Martín Plágaro, César Augusto | |
dc.date.accessioned | 2024-05-02T18:02:29Z | |
dc.date.available | 2024-05-02T18:02:29Z | |
dc.date.issued | 2023-10 | |
dc.identifier.citation | Current Atherosclerosis Reports 25 : 839-859 (2023) | es_ES |
dc.identifier.issn | 1534-6242 | |
dc.identifier.issn | 1523-3804 | |
dc.identifier.uri | http://hdl.handle.net/10810/67320 | |
dc.description.abstract | Purpose of Review Familial hypercholesterolemia (FH) is a hereditary condition characterized by elevated levels of low- density lipoprotein cholesterol (LDL-C), which increases the risk of cardiovascular disease if left untreated. This review aims to discuss the role of bioinformatics tools in evaluating the pathogenicity of missense variants associated with FH. Specifically, it highlights the use of predictive models based on protein sequence, structure, evolutionary conservation, and other relevant features in identifying genetic variants within LDLR, APOB, and PCSK9 genes that contribute to FH. Recent Findings In recent years, various bioinformatics tools have emerged as valuable resources for analyzing missense variants in FH-related genes. Tools such as REVEL, Varity, and CADD use diverse computational approaches to predict the impact of genetic variants on protein function. These tools consider factors such as sequence conservation, structural altera- tions, and receptor binding to aid in interpreting the pathogenicity of identified missense variants. While these predictive models offer valuable insights, the accuracy of predictions can vary, especially for proteins with unique characteristics that might not be well represented in the databases used for training. Summary This review emphasizes the significance of utilizing bioinformatics tools for assessing the pathogenicity of FH- associated missense variants. Despite their contributions, a definitive diagnosis of a genetic variant necessitates functional validation through in vitro characterization or cascade screening. This step ensures the precise identification of FH-related variants, leading to more accurate diagnoses. Integrating genetic data with reliable bioinformatics predictions and functional validation can enhance our understanding of the genetic basis of FH, enabling improved diagnosis, risk stratification, and personalized treatment for affected individuals. The comprehensive approach outlined in this review promises to advance the management of this inherited disorder, potentially leading to better health outcomes for those affected by FH. | es_ES |
dc.description.sponsorship | Open Access funding provided thanks to the CRUE-CSIC agreement with Springer Nature. This research was funded by Grupos Consolidados Gobierno Vasco 2021, grant number IT1720-22. A.L.-S. was supported by a grant PIF (2019–2020), Gobierno Vasco and partially supported by Fundación Biofísica Bizkaia. S.J-B. was supported by a Margarita Salas Grant 2022 from the University of the Basque Country. | es_ES |
dc.language.iso | eng | es_ES |
dc.publisher | Springer Nature | es_ES |
dc.rights | info:eu-repo/semantics/openAccess | es_ES |
dc.rights.uri | http://creativecommons.org/licenses/by/3.0/es/ | * |
dc.subject | familial hypercholesterolemia | es_ES |
dc.subject | LDLR | es_ES |
dc.subject | APOB | es_ES |
dc.subject | PCSK9 | es_ES |
dc.subject | bioinformatics tools | es_ES |
dc.subject | functional validation | es_ES |
dc.title | Predictive Modeling and Structure Analysis of Genetic Variants in Familial Hypercholesterolemia: Implications for Diagnosis and Protein Interaction Studies | es_ES |
dc.type | info:eu-repo/semantics/article | es_ES |
dc.rights.holder | © The Author(s) 2023. This article is licensed under a Creative Commons Attri- bution 4.0 International License, which permits use, sharing, adapta- tion, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. | es_ES |
dc.rights.holder | Atribución 3.0 España | * |
dc.relation.publisherversion | https://link.springer.com/article/10.1007/s11883-023-01154-7 | es_ES |
dc.identifier.doi | 10.1007/s11883-023-01154-7 | |
dc.departamentoes | Bioquímica y biología molecular | es_ES |
dc.departamentoeu | Biokimika eta biologia molekularra | es_ES |
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