Predictive Modeling and Structure Analysis of Genetic Variants in Familial Hypercholesterolemia: Implications for Diagnosis and Protein Interaction Studies

[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 eval...

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Autores: Larrea, Asier, Jebari-Benslaiman, Shifa, Galicia-García, Unai, San Jose-Urteaga, Ane, Uribe, Kepa B., Benito-Vicente, Asier, Martín, César
Tipo de recurso: artículo
Estado:Versión publicada
Fecha de publicación:2023
País:España
Institución:Consejo Superior de Investigaciones Científicas (CSIC)
Repositorio:DIGITAL.CSIC. Repositorio Institucional del CSIC
OAI Identifier:oai:digital.csic.es:10261/344024
Acceso en línea:http://hdl.handle.net/10261/344024
Access Level:acceso abierto
Palabra clave:Familial hypercholesterolemia
LDLR
APOB
PCSK9
Bioinformatics tools
Functional validation
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spelling Predictive Modeling and Structure Analysis of Genetic Variants in Familial Hypercholesterolemia: Implications for Diagnosis and Protein Interaction StudiesLarrea, AsierJebari-Benslaiman, ShifaGalicia-García, UnaiSan Jose-Urteaga, AneUribe, Kepa B.Benito-Vicente, AsierMartín, CésarFamilial hypercholesterolemiaLDLRAPOBPCSK9Bioinformatics toolsFunctional validation[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 alterations, 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.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.Peer reviewedSpringer NatureConsejo Superior de Investigaciones Científicas (España)Conferencia de Rectores de las Universidades EspañolasEusko JaurlaritzaFundación Biofísica BizkaiaUniversidad del País VascoConsejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]202420242023info:eu-repo/semantics/articlehttp://purl.org/coar/resource_type/c_dcae04bcPublisher's versioninfo:eu-repo/semantics/publishedVersionapplication/pdfhttp://hdl.handle.net/10261/344024reponame:DIGITAL.CSIC. Repositorio Institucional del CSICinstname:Consejo Superior de Investigaciones Científicas (CSIC)Ingléshttps://doi.org/10.1007/s11883-023-01154-7Síinfo:eu-repo/semantics/openAccessoai:digital.csic.es:10261/3440242026-05-22T06:33:51Z
dc.title.none.fl_str_mv Predictive Modeling and Structure Analysis of Genetic Variants in Familial Hypercholesterolemia: Implications for Diagnosis and Protein Interaction Studies
title Predictive Modeling and Structure Analysis of Genetic Variants in Familial Hypercholesterolemia: Implications for Diagnosis and Protein Interaction Studies
spellingShingle Predictive Modeling and Structure Analysis of Genetic Variants in Familial Hypercholesterolemia: Implications for Diagnosis and Protein Interaction Studies
Larrea, Asier
Familial hypercholesterolemia
LDLR
APOB
PCSK9
Bioinformatics tools
Functional validation
title_short Predictive Modeling and Structure Analysis of Genetic Variants in Familial Hypercholesterolemia: Implications for Diagnosis and Protein Interaction Studies
title_full Predictive Modeling and Structure Analysis of Genetic Variants in Familial Hypercholesterolemia: Implications for Diagnosis and Protein Interaction Studies
title_fullStr Predictive Modeling and Structure Analysis of Genetic Variants in Familial Hypercholesterolemia: Implications for Diagnosis and Protein Interaction Studies
title_full_unstemmed Predictive Modeling and Structure Analysis of Genetic Variants in Familial Hypercholesterolemia: Implications for Diagnosis and Protein Interaction Studies
title_sort Predictive Modeling and Structure Analysis of Genetic Variants in Familial Hypercholesterolemia: Implications for Diagnosis and Protein Interaction Studies
dc.creator.none.fl_str_mv Larrea, Asier
Jebari-Benslaiman, Shifa
Galicia-García, Unai
San Jose-Urteaga, Ane
Uribe, Kepa B.
Benito-Vicente, Asier
Martín, César
author Larrea, Asier
author_facet Larrea, Asier
Jebari-Benslaiman, Shifa
Galicia-García, Unai
San Jose-Urteaga, Ane
Uribe, Kepa B.
Benito-Vicente, Asier
Martín, César
author_role author
author2 Jebari-Benslaiman, Shifa
Galicia-García, Unai
San Jose-Urteaga, Ane
Uribe, Kepa B.
Benito-Vicente, Asier
Martín, César
author2_role author
author
author
author
author
author
dc.contributor.none.fl_str_mv Consejo Superior de Investigaciones Científicas (España)
Conferencia de Rectores de las Universidades Españolas
Eusko Jaurlaritza
Fundación Biofísica Bizkaia
Universidad del País Vasco
Consejo Superior de Investigaciones Científicas [https://ror.org/02gfc7t72]
dc.subject.none.fl_str_mv Familial hypercholesterolemia
LDLR
APOB
PCSK9
Bioinformatics tools
Functional validation
topic Familial hypercholesterolemia
LDLR
APOB
PCSK9
Bioinformatics tools
Functional validation
description [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.
publishDate 2023
dc.date.none.fl_str_mv 2023
2024
2024
dc.type.none.fl_str_mv info:eu-repo/semantics/article
http://purl.org/coar/resource_type/c_dcae04bc
Publisher's version
info:eu-repo/semantics/publishedVersion
format article
status_str publishedVersion
dc.identifier.none.fl_str_mv http://hdl.handle.net/10261/344024
url http://hdl.handle.net/10261/344024
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv https://doi.org/10.1007/s11883-023-01154-7
Sí
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
eu_rights_str_mv openAccess
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv Springer Nature
publisher.none.fl_str_mv Springer Nature
dc.source.none.fl_str_mv reponame:DIGITAL.CSIC. Repositorio Institucional del CSIC
instname:Consejo Superior de Investigaciones Científicas (CSIC)
instname_str Consejo Superior de Investigaciones Científicas (CSIC)
reponame_str DIGITAL.CSIC. Repositorio Institucional del CSIC
collection DIGITAL.CSIC. Repositorio Institucional del CSIC
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15.811543

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